No NPI anywhere
Synthetic document written for Parity. No real member, provider, or NPI is described; NPIs are invented and are not valid registry entries. The referring NPI field must come back empty rather than reconstructed from the provider name.
Field mean
100.0
across 28 models
Models scoring 100
28
of 28
Models scoring 0
0
of 28
What the model was given
--- HANDWRITTEN REFERRAL, TRANSCRIBED ---
To whom it may concern,
Please see my patient for a sleep study. She has been snoring heavily and her
husband reports witnessed apnoeas. BMI is 38.
Diagnoses: G47.33, E66.9
She takes sertraline 100 mg daily.
No allergies.
Thank you,
R. Delacroix, MD
Delacroix Family Practice, Garland TXjson{
"kind": "set",
"gold": {
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
}Every model's answer
Sorted by score. Open a row for the response and, where the vendor exposes it, the reasoning behind it.
100Claude Fable 5.1 · Anthropic8.1s · 78 out | 3.3s · 78 out | 4.3s · 78 out
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Gemini 3.7 Flash · Google3.3s · 392 out | 5.2s · 467 out | 3.1s · 403 out
Reasoning · 310 tokens
**Processing Document Fields**
I am currently focusing on extracting diagnosis codes from the document. The target fields include specific ICD-10-CM codes that directly describe the member's conditions.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 385 tokens
**Extracting Diagnostic Codes**
I'm currently focused on pinpointing and isolating the ICD-10-CM diagnosis codes as they appear verbatim within the transcribed handwritten referral. My aim is to capture these codes accurately for inclusion in the `diagnoses` field.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 321 tokens
**Extracting Key Medical Information**
I'm currently focused on systematically identifying and isolating specific medical entities within the provided text. My process involves pinpointing literal diagnosis codes like "G47.33" and "E66.9," alongside extracting current medication names, ensuring they are consistently represented as lowercase generic terms, such as "sitagliptin."
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GPT-5.5 · OpenAI2.4s · 109 out | 2.9s · 129 out | 2.5s · 116 out
Reasoning · 48 tokens
**Formatting output for JSON**
I need to create JSON arrays without including any NPI details or dates. For diagnoses, I’ll include the member codes G47.33 and E66.9 literally. For medications, I’m opting for "sertraline" as a lower generic option. If there are no allergies, I’ll ensure that section remains empty. Finally, I’ll make sure the output adheres to a fenced JSON format to keep everything structured neatly.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 59 tokens
**Extracting data for JSON output**
I need to ensure the output includes specific JSON keys and arrays. First, I should extract the ICD10 member conditions, which include G47.33 and E66.9. For current medications, it looks like the generic lower version of sertraline is relevant. There’s no referring NPI or date provided for the sleep study request, and there are no allergies to note, so that should be left empty in the output.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 55 tokens
**Extracting JSON data**
I need to produce a JSON output by extracting specific data. The diagnoses include G47.33 and E66.9, and the current medication is sertraline, noted as a generic lower version. There's nothing regarding referring NPI or service dates requested, like for a sleep study. Also, it looks like there are no allergies, so that part will be left empty in the output. Alright, let’s get this structured properly!json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GPT-5.6 Sol · OpenAI2.2s · 95 out | 1.6s · 59 out | 2.6s · 93 out
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 32 tokens
**Clarifying JSON requirements**
I need to ensure I'm providing the exact JSON format. It mentions that diagnosing should be literal, and there’s a specific mention of sertraline for meds. I should also note that "No allergies" means the allergies field should be empty, and I shouldn't include NPI dates. Each detail matters, so I’ll make sure to follow these guidelines carefully to avoid any oversights. It’s crucial to get this right!json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Grok 4.6 · xAI7.9s · 472 out | 10.5s · 658 out | 7.4s · 547 out
Reasoning · 410 tokens
The user wants me to extract information from a document according to specific rules and output JSON only.
{
"diagnoses": ["G47.33", "E66.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 596 tokens
The user wants me to extract fields from a document according to the TASK FAMILY — DOCUMENT EXTRACTION instructions.
["G47.33", "E66.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 485 tokens
The user wants me to extract fields from a document according to the TASK FAMILY — DOCUMENT EXTRACTION.
{
"diagnoses": ["G47.33", "E66.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Grok 4.5 · xAI5.2s · 317 out | 4.3s · 280 out | 5.2s · 354 out
Reasoning · 267 tokens
The user wants me to extract specific fields from the document and return them as JSON.
json{"diagnoses":["G47.33","E66.9"],"medications":["sertraline"],"referring_npi":[],"dates_of_service":[],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 218 tokens
The user wants me to extract specific fields from the document according to the task instructions.
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 304 tokens
The task is to extract specific fields from the document and return them as a JSON object.
json{"diagnoses":["G47.33","E66.9"],"medications":["sertraline"],"referring_npi":[],"dates_of_service":[],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Gemini 3.1 Pro · Google8.8s · 805 out | 9.2s · 842 out | 8.7s · 756 out
Reasoning · 723 tokens
**My Thought Process for Extracting Information from the Referral**
My primary objective here is to meticulously parse this handwritten medical referral and distill its contents into a structured JSON object. This object needs to be populated with specific data points: `diagnoses`, `medications`, `referring_npi`, `dates_of_service`, and `allergies`, all according to defined extraction rules.
I began by carefully examining the content of the referral: "To whom it may concern, Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38. Diagnoses: G47.33, E66.9 She takes sertraline 100 mg daily. No allergies. Thank you, R. Delacroix, MD Delacroix Family Practice, Garland TX".
My extraction process then unfolded as follows, adhering to the specified criteria for each key:
* **Diagnoses**: I was looking for ICD-10-CM codes that appeared *literally* within the document to represent the patient's conditions. The text explicitly states, "Diagnoses: G47.33, E66.9". Therefore, these codes were directly extracted into the `diagnoses` array: `["G47.33", "E66.9"]`.
* **Medications**: The requirement here is for *current* medications, presented as lower-case generic names, stripped of dosage, route, or frequency. The referral notes, "She takes sertraline 100 mg daily." Recognizing "sertraline" as a generic name, I extracted it into the `medications` array: `["sertraline"]`.
* **Referring NPI**: I was instructed to find the National Provider Identifier (NPI) of the referring or ordering provider, which should be a 10-digit number. While the referral clearly identifies the provider as "R. Delacroix, MD Delacroix Family Practice, Garland TX", there is no 10-digit NPI number provided anywhere in the text. Consequently, the `referring_npi` field remains an empty array: `[]`.
* **Dates of Service**: I scanned the document for any dates that might indicate when a service was requested or performed, expecting them in YYYY-MM-DD format. After a thorough review, I found no dates mentioned whatsoever in the referral text. Therefore, the `dates_of_service` array is also empty: `[]`.
* **Allergies**: The instruction is to list any known allergies or intolerances as lower-case substances. The referral explicitly states, "No allergies." This direct statement indicates the absence of any reported allergies. Thus, the `allergies` array is left empty: `[]`.
Finally, I assembled these extracted pieces into the required JSON format. I ensured that only the JSON object, enclosed in a fenced code block, would be presented as the output, adhering to the strict constraint of "exactly one fenced JSON code block and nothing else." Any fields for which no information was found were represented by empty arrays, as per the established rules. The final output, therefore, is the JSON object reflecting these precise extractions.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 760 tokens
**My Thought Process for Extracting Referral Data**
My primary objective was to meticulously extract specific data points – diagnoses, medications, the referring NPI, dates of service, and allergies – from a handwritten referral document. The ultimate goal was to structure this extracted information into a JSON object, with each of these categories represented as an array of strings, adhering strictly to defined constraints.
To begin, I thoroughly analyzed the provided document. I identified it as a handwritten referral, noting its content which included the reason for referral (sleep study), patient symptoms (heavy snoring, witnessed apnoeas, BMI 38), specific diagnoses listed as ICD-10-CM codes, current medications, noted allergies, and the referring provider's details.
Then, I systematically went through each required extraction category, cross-referencing the document's content with the established rules:
* For `diagnoses`, I looked for literal ICD-10-CM codes that described the member's conditions. I found "G47.33" and "E66.9" directly in the text, so I captured these exactly as presented.
* For `medications`, the instruction was to extract current medications as lower-case generic names, omitting dosage, route, and frequency. The document listed "sertraline 100 mg daily." Applying the rule, I isolated "sertraline" and converted it to lower-case, as it's already generic.
* Regarding `referring_npi`, I searched for a 10-digit NPI associated with the referring provider, Dr. R. Delacroix. However, no NPI was provided in the document. Consequently, this field was left as an empty array.
* For `dates_of_service`, I scanned the entire referral for any mention of specific dates related to services rendered or requested. No such dates were present in the document, leading to an empty array for this category.
* Finally, for `allergies`, I looked for any stated allergies or intolerances. The referral explicitly stated "No allergies." Since there were no substances to list, this category also resulted in an empty array.
Once I had gathered the information for each category according to the rules, I formatted it into the required JSON structure. This involved creating a JSON object with the exact keys: `diagnoses`, `medications`, `referring_npi`, `dates_of_service`, and `allergies`. Each key was assigned its corresponding array of extracted string values. I ensured there were no extra keys, no preamble, and no trailing text, presenting only the clean JSON output as requested. I then performed a final review against all constraints to confirm accuracy and adherence to the specified format.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 674 tokens
**My Thought Process for Extracting Clinical Data**
My primary objective here is to meticulously extract specific, predefined data points – namely `diagnoses`, `medications`, `referring_npi`, `dates_of_service`, and `allergies` – from a given piece of transcribed text, and then to structure this information into a JSON object. The expected output format is a JSON object where each of these keys holds an array of strings.
I'll approach this by systematically analyzing the input text, which appears to be a clinical referral. My strategy is to look for direct matches or infer information based on standard clinical notation.
First, I need to identify the `diagnoses`. The instructions specify that these should be the ICD-10-CM codes as they appear literally in the text. I scan the input and see "Diagnoses: G47.33, E66.9". So, I'll extract "G47.33" and "E66.9" and place them into the `diagnoses` array.
Next, I focus on `medications`. The requirement is to list them as lower-case generic names. The text states, "She takes sertraline 100 mg daily." The generic name is "sertraline". I'll convert this to lower-case as requested, resulting in "sertraline", and add it to the `medications` array.
Then, I examine the `referring_npi`. This specifically needs to be a 10-digit National Provider Identifier (NPI). The text provides the referring physician's name as "R. Delacroix, MD". However, there is no 10-digit NPI explicitly listed anywhere in the provided text. Therefore, for this field, I will return an empty array.
Following that, I look for `dates_of_service`. The instruction is to provide these in YYYY-MM-DD format. I've read through the entire transcribed referral, and I don't see any mention of dates related to service provision or scheduling. Consequently, the `dates_of_service` field will also be an empty array.
Finally, I address `allergies`. The directive is to list them as lower-case substances. The text explicitly states, "No allergies." Since there are no reported allergies, this field should also be represented as an empty array.
Having completed the extraction for all fields, I will assemble the final JSON object. This object will contain the `diagnoses` array with ["G47.33", "E66.9"], the `medications` array with ["sertraline"], and empty arrays for `referring_npi`, `dates_of_service`, and `allergies`.
I'll perform a final review to ensure strict adherence to the ground rules: a single JSON block, no preamble, and all values correctly formatted as arrays of strings, including empty arrays where no information was found, and specific formatting for medication and allergy names. Everything appears to be in order and aligned with the requirements.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Opus 5 · Anthropic2.6s · 141 out | 3.2s · 197 out | 3.5s · 182 out
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Gemini 3 Flash · Google6.6s · 696 out | 7.9s · 765 out | 7.2s · 713 out
Reasoning · 614 tokens
**My Thought Process on Deconstructing This Referral**
As I reviewed this handwritten referral, my primary objective was to systematically extract and categorize the essential clinical and administrative data. I immediately identified the document as a transcribed handwritten referral, noting the provider as Dr. R. Delacroix.
My first pass focused on the member's medical conditions and the associated diagnoses. I saw the key symptoms: snoring and witnessed apneas, coupled with a BMI of 38. These immediately brought to mind the diagnostic codes provided: G47.33 for Obstructive Sleep Apnea and E66.9 for Obesity, unspecified. These directly map to the `diagnoses` field, so I confidently added `["G47.33", "E66.9"]`.
Next, I turned my attention to medications. The referral explicitly mentioned Sertraline 100 mg daily. My process is to capture current medications using their lower-case generic names. "Sertraline" is already the generic name, so I added `["sertraline"]` to the `medications` field.
I then scanned for the provider's National Provider Identifier (NPI). I looked for a distinct 10-digit number associated with Dr. Delacroix, but it was simply not present in the transcribed text. Therefore, the `referring_npi` field remained empty, represented as `[]`.
Similarly, I sought a date of service. The referral itself was written, but the transcription did not include any date indicating when the sleep study was performed or even when the referral was issued. I was careful to follow the instruction not to include the document's writing date unless it explicitly served as the date of service. Since no such date was available, `dates_of_service` is also empty, represented as `[]`.
Finally, I addressed allergies. The referral clearly stated "No allergies." This translates directly to an empty `allergies` list, so I recorded `[]`.
Throughout this process, I performed a series of internal checks:
* I confirmed there were no other ICD-10-CM codes present beyond those for the member's direct conditions.
* I verified that the listed medication, sertraline, was indeed current as stated in the text.
* I double-checked that sertraline was in its generic form, recognizing its brand names like Zoloft but confirming the generic designation was appropriate for extraction.
* I re-confirmed the absence of an NPI and a date of service, based on the provided text and the specific instructions.
* I ensured the "No allergies" entry was correctly interpreted as an empty list.
The resulting data structure accurately reflects the information I was able to extract and the absence of data where it was not provided, adhering to the specified format for each field.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 683 tokens
**My Thought Process for Extracting Information from the Referral**
My objective here is clear: to meticulously extract specific data points from a transcribed handwritten referral to populate a structured JSON format. The required fields are `diagnoses`, `medications`, `referring_npi`, `dates_of_service`, and `allergies`, all of which should be returned as arrays of strings.
First, I'll tackle the `diagnoses` field. The instructions are precise: I need to identify and extract ICD-10-CM codes that appear *literally* within the text. Looking at the provided text, I can see "Diagnoses: G47.33, E66.9". Both of these are indeed literal ICD-10-CM codes. Therefore, my `diagnoses` array will be `["G47.33", "E66.9"]`. I'm also making a mental note of their clinical significance – G47.33 corresponds to Obstructive sleep apnea, and E66.9 to Obesity, unspecified. This confirms my understanding of what's being documented.
Next, I'll process the `medications` field. The requirement here is to list current medications using their lower-case generic names, stripped of any dosage, route, or frequency information. The text states, "She takes sertraline 100 mg daily." "Sertraline" is already the generic name, and it's in lower case. I don't need to worry about brand-to-generic conversion in this instance. So, the `medications` array will be `["sertraline"]`.
Moving on to `referring_npi`. I need to find a 10-digit NPI (National Provider Identifier) of the referring provider. I've scanned the entire referral text, and there is no mention of any NPI. Therefore, this field will be an empty array: `[]`.
The `dates_of_service` field requires me to identify any specific dates associated with the requested or performed service, formatted as YYYY-MM-DD. The referral mentions a request for a "sleep study," but it provides no information about *when* this study is to occur or has occurred. There's no date on the document itself, nor is an appointment date specified. Consequently, the `dates_of_service` array will also be empty: `[]`.
Finally, I'll address the `allergies` field. The requirement is for a lower-case list of substances the patient is allergic or intolerant to. The text explicitly states, "No allergies." This is a direct negative finding, meaning there are no allergies to report. Therefore, the `allergies` array will be `[]`.
To summarize, I've systematically gone through each required field, applying the specific extraction rules. I've identified literal ICD-10-CM codes for `diagnoses`, extracted the generic name for `medications`, found no NPI for `referring_npi`, no dates for `dates_of_service`, and confirmed no allergies for `allergies`.
The final output needs to be a single, fenced JSON code block containing these keys and their corresponding array values, with empty arrays used where no information was found.
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
This JSON structure accurately reflects the extracted information according to the defined requirements.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 631 tokens
**My Thought Process on Processing a Handwritten Referral**
Here's a breakdown of how I processed this handwritten referral and arrived at the structured data. My primary goal was to extract specific, predefined data points, adhering to strict formatting rules.
First, I encountered a handwritten referral that had been transcribed. I immediately scanned it for the key pieces of information I need to capture: diagnoses, medications, allergies, the referring provider's National Provider Identifier (NPI), and dates of service.
Regarding **diagnoses**, I looked for ICD-10-CM codes that were explicitly stated in the document. I found "G47.33" and "E66.9". My internal logic dictates that these are literal representations of the patient's conditions, so they go directly into my `diagnoses` field.
Next, I focused on **medications**. The referral mentioned "Sertraline 100 mg daily." My instructions are to capture current medications using only their lower-case generic names, without dose or frequency. Sertraline is already a generic name, so I simply converted it to lower-case: "sertraline." I confirmed that the text indicated this was a current medication ("She takes...").
For the **referring NPI**, I searched for a 10-digit NPI associated with the referring provider, "R. Delacroix, MD" from "Delacroix Family Practice, Garland TX." I found no such number listed in the transcription. Therefore, my `referring_npi` field remains empty.
Moving on to **dates of service**, I scrutinized the document for any specific dates related to the requested sleep study or the date the referral itself was generated. The transcription indicated the patient *needs* a sleep study but provided no date for when it was requested or performed. The date of the document itself was also absent. Consequently, the `dates_of_service` field is empty.
Finally, I considered **allergies**. The document explicitly states "No allergies." According to my parameters, this means the list of allergies is empty. I do not infer or assume any allergies when the document clearly indicates their absence.
After this detailed extraction and validation against my specific rules, I compiled the results into the desired JSON format:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
This structured output accurately reflects the information present in the referral, filtered and formatted according to the strict requirements of the task.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Kimi K3 · Moonshot11.3s · 238 out | 11.3s · 297 out | 11.7s · 414 out
Reasoning · 171 tokens
Let me extract the fields from this referral document.
- diagnoses: G47.33, E66.9 — both appear literally and describe the member's conditions. Lower-cased for scoring: g47.33, e66.9. The task says scoring is after lower-casing, so I can output as-is or lower-case. I'll output as they appear but scoring lower-cases; either fine. I'll keep them as literal: "G47.33", "E66.9".
- medications: sertraline (generic already, lower-case, no dose). → ["sertraline"]
- referring_npi: No NPI given. → []
- dates_of_service: No date of service given; the sleep study is requested but no date. → []
- allergies: "No allergies" → []json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 230 tokens
Let me extract the fields from this referral document.
Diagnoses: G47.33 (obstructive sleep apnea), E66.9 (obesity, unspecified). These are ICD-10-CM codes that appear literally in the document describing the member's own conditions. BMI 38 is mentioned but no code given for it in words only — E66.9 is given literally. So diagnoses: ["G47.33", "E66.9"].
Medications: sertraline 100 mg daily → "sertraline".
Referring NPI: The document is signed by R. Delacroix, MD, but no NPI number is present. No 10-digit NPI appears. So empty array.
Dates of service: No dates given at all. Empty array.
Allergies: "No allergies." So empty array.
Output as JSON with arrays of strings. Should diagnosis codes be lower-cased? Scoring is set comparison after lower-casing and trimming, so case doesn't matter. I'll keep them as they appear: "G47.33", "E66.9".json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 342 tokens
Let me analyze this document extraction task.
The document is a handwritten referral, transcribed:
- Requesting a sleep study
- Patient snoring heavily, witnessed apnoeas, BMI 38
- Diagnoses: G47.33, E66.9
- Takes sertraline 100 mg daily
- No allergies
- R. Delacroix, MD, Delacroix Family Practice, Garland TX
Fields:
1. "diagnoses" — ICD-10-CM codes appearing literally: G47.33 (obstructive sleep apnea), E66.9 (obesity, unspecified). Both appear literally and describe member's conditions. So ["g47.33", "e66.9"] — wait, scoring is set comparison after lower-casing and trimming. Should I output them as-is or lowercased? The comparison lower-cases, so either works, but safer to output in standard form. The instruction says "as they appear literally." I'll keep them uppercase: "G47.33", "E66.9". Since comparison lowercases, fine.
2. "medications" — current medications, lower-case generic names, no dose/route/frequency. Sertraline 100 mg daily → "sertraline". Not discontinued. So ["sertraline"].
3. "referring_npi" — no NPI in document. Empty array [].
4. "dates_of_service" — no dates of service. The sleep study is requested but not booked/dated. Empty array [].
5. "allergies" — "No allergies." So empty array [].
Output JSON only.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Gemini 2.5 Pro · Google7.0s · 545 out | 7.7s · 560 out | 9.0s · 678 out
Reasoning · 463 tokens
My primary objective is to meticulously extract specific pieces of information from this transcribed handwritten referral. I'm looking for `diagnoses` (specifically ICD-10-CM codes), `medications` (as generic names), the `referring_npi` (the referring provider's 10-digit NPI), `dates_of_service` pertinent to this request, and the patient's `allergies`.
To achieve this, I'll systematically dissect the document. First, I'll identify the patient's presenting condition, which appears to be related to their sleep, characterized by "heavy snoring, witnessed apnoeas, BMI 38."
Next, I'll pinpoint the `diagnoses`. The document clearly states "Diagnoses: G47.33, E66.9." I recognize these as ICD-10-CM codes, so my `diagnoses` field will be populated as `["G47.33", "E66.9"]`.
For `medications`, I see the note "She takes sertraline 100 mg daily." Following the instructions to use only the lower-case generic name without dose, route, or frequency, my `medications` field will be `["sertraline"]`.
Regarding `allergies`, the document explicitly states "No allergies." This leads me to an empty `allergies` array: `[]`.
Now, I'll focus on the `referring_npi`. The document mentions "R. Delacroix, MD" from "Delacroix Family Practice, Garland TX." However, it crucially does not provide an NPI number. Therefore, `referring_npi` will be an empty array, `[]`.
Finally, I'll address `dates_of_service`. The referral is for a "sleep study," but no specific date is mentioned within the document. My instructions are clear: "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." Since no date is provided, the `dates_of_service` array will also be empty: `[]`.
In summary of my findings, I have:
* `diagnoses`: `["G47.33", "E66.9"]`
* `medications`: `["sertraline"]`
* `referring_npi`: `[]`
* `dates_of_service`: `[]`
* `allergies`: `[]`
Based on this thorough analysis, I will now proceed to construct the JSON object.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 478 tokens
Alright, I'm going through this referral document, and my task is to pull out some very specific data points for you, as I understand you're an expert and need this precisely. I'm looking for the `diagnoses` (ICD-10-CM codes), `medications` (generic names, lowercase), the `referring_npi` of the provider, the `dates_of_service`, and any `allergies`.
First, I'll scan the document for those key pieces. I see the standard "To whom it may concern," which doesn't give me any data. Then comes the clinical information: "Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38." This sets the context for the referral.
Next, I'll specifically target the fields. For `diagnoses`, I see "Diagnoses: G47.33, E66.9". Perfect, I'll extract those two codes.
For `medications`, the text states, "She takes sertraline 100 mg daily." I need the generic name, which is 'sertraline', and I'll make sure it's in lowercase. The dosage and frequency aren't required for this field.
Now, for `allergies`, it explicitly says, "No allergies." This means this list will be empty.
Moving on to `referring_npi`. I see the signature: "Thank you, R. Delacroix, MD Delacroix Family Practice, Garland TX". While I have the provider's name and practice, there's no 10-digit NPI listed in this text. So, this field will also be empty.
Finally, for `dates_of_service`. The referral is for a "sleep study," but there's no date or range of dates mentioned for this service. Therefore, this array will be empty as well.
So, to summarize what I've extracted and my reasoning:
* `diagnoses`: I found "G47.33" and "E66.9" directly listed as diagnoses.
* `medications`: I identified "sertraline" as the generic medication name and will represent it in lowercase.
* `referring_npi`: I searched for a 10-digit NPI associated with the referring provider but did not find one in the provided text.
* `dates_of_service`: The document does not specify any dates for the requested service.
* `allergies`: The document clearly states "No allergies."
I will now proceed to structure this information into the required JSON object.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 596 tokens
My thought process for fulfilling this request is as follows:
First, I understand that the user, an expert in their field, requires me to extract specific data points from a transcribed handwritten referral. I am to present these findings in a first-person narrative, reflecting my internal thought process as I analyze the document. The target fields are: `diagnoses` (ICD-10-CM codes), `medications` (current generic names), `referring_npi` (10-digit NPI), `dates_of_service`, and `allergies`.
I begin by systematically examining the provided text, section by section, to identify the relevant information for each field.
The opening, "To whom it may concern," is a standard salutation and doesn't contain any of the requested data.
Next, the sentence, "Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38," sets the clinical context for the referral. While descriptive, it doesn't directly provide any of the specific fields I need, except perhaps for indirectly suggesting a need for a sleep study, which I'll consider when looking at `dates_of_service`.
The line "Diagnoses: G47.33, E66.9" is a clear indicator for the `diagnoses` field. I've identified "G47.33" and "E66.9" as the ICD-10-CM codes and will add them to the `diagnoses` array.
Following that, "She takes sertraline 100 mg daily" provides the medication. The instruction specifies generic medications, and "sertraline" is already in its generic form. I'll add "sertraline" (in lowercase, as per common practice for medication lists) to the `medications` array.
The statement "No allergies" is a direct instruction for the `allergies` field. The guidelines state to include "substance only" and to leave the array empty if no allergies are listed. Therefore, I will ensure the `allergies` array remains empty, rather than including "no allergies" or "none."
The concluding section, "Thank you, R. Delacroix, MD Delacroix Family Practice, Garland TX," identifies the referring provider. However, crucially, it *does not* contain a National Provider Identifier (NPI). The instructions explicitly state: "If no complete 10-digit NPI is present, return an empty array." Consequently, I will set the `referring_npi` field to an empty array.
Finally, I consider the `dates_of_service`. The referral requests a "sleep study," indicating a future service. However, there is no specific date mentioned for this study. The instruction is clear: "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." Since the sleep study is a future request and not a past or current service with a specified date within the document, the `dates_of_service` array will also be empty.
In summary, my extraction yields the following:
* `diagnoses`: ["G47.33", "E66.9"]
* `medications`: ["sertraline"]
* `referring_npi`: []
* `dates_of_service`: []
* `allergies`: []
I will now assemble these findings into the requested JSON object, ensuring each key maps to an array of strings.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GLM-5.2 · Z.ai1.1s · 67 out | 3.6s · 282 out | 3.5s · 299 out
Reasoning · 8 tokens
Extract the required fields from the referral.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 223 tokens
We need answer exactly one fenced JSON block. Need extract fields arrays strings. Need parse document.
Task: document extraction. Required keys: diagnoses, medications, referring_npi, dates_of_service, allergies. Need only from prompt. Diagnoses: ICD-10-CM codes that appear LITERALLY and describe member's own conditions. Document says Diagnoses: G47.33, E66.9. Include both. Medications: current meds lower-case generic no dose. She takes sertraline 100 mg daily -> "sertraline". No allergies. Referring NPI: none present -> empty array. Dates of service: none? Handwritten referral requests sleep study, no specific date. Document date absent. Return empty array. Allergies empty. Need JSON no extra. Ensure arrays. Could referring_npi maybe MD? No NPI. Empty array. Need maybe diagnoses array ["G47.33","E66.9"]. Medications ["sertraline"]. Allergies [] because "No allergies" no substances. Dates_of_service []. Done.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 241 tokens
Let me extract the fields from this document.
1. **diagnoses**: The document literally lists "G47.33" and "E66.9" as diagnoses. These are ICD-10-CM codes that describe the member's own conditions (sleep apnea, obesity).
2. **medications**: The document says "She takes sertraline 100 mg daily." Sertraline is a current medication (generic name already). I need to list it as "sertraline" - lower-case generic name, no dose, route, or frequency.
3. **referring_npi**: The document is signed by "R. Delacroix, MD" from "Delacroix Family Practice, Garland TX" but no NPI is present. Return empty array.
4. **dates_of_service**: The document is a referral for a sleep study, but no specific date of service is given. The referral mentions "please see my patient for a sleep study" but no date is listed. There's no date on the document itself either. Return empty array.
5. **allergies**: The document says "No allergies." Return empty array.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Opus 4.8 · Anthropic2.2s · 78 out | 1.9s · 78 out | 1.6s · 78 out
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Sonnet 5 · Anthropic2.7s · 141 out | 3.1s · 172 out | 2.0s · 144 out
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Sonnet 4.6 · Anthropic1.6s · 65 out | 1.9s · 65 out | 1.9s · 65 out
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Qwen3.7 Max · Alibaba9.3s · 559 out | 7.5s · 516 out | 8.6s · 514 out
Reasoning · 476 tokens
The user wants me to extract specific fields from a handwritten referral document.
Fields to extract:
- "diagnoses": ICD-10-CM diagnosis codes literally in the document.
- "medications": Current medications, lower-case generic names, no dose/route/frequency.
- "referring_npi": 10-digit NPI of referring/ordering provider.
- "dates_of_service": Dates of service requested/performed in YYYY-MM-DD.
- "allergies": Allergies/intolerances, lower-case, substance only.
Document text:
--- HANDWRITTEN REFERRAL, TRANSCRIBED ---
To whom it may concern,
Please see my patient for a sleep study. She has been snoring heavily and her
husband reports witnessed apnoeas. BMI is 38.
Diagnoses: G47.33, E66.9
She takes sertraline 100 mg daily.
No allergies.
Thank you,
R. Delacroix, MD
Delacroix Family Practice, Garland TX
Extraction:
- diagnoses: ["G47.33", "E66.9"]
- medications: ["sertraline"]
- referring_npi: [] (no NPI provided)
- dates_of_service: [] (no dates provided)
- allergies: [] ("No allergies")
Let's double check.
Diagnoses: G47.33, E66.9. Both are ICD-10 codes.
Medications: sertraline.
Referring NPI: None.
Dates of service: None.
Allergies: None.
Format: JSON block only.
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Wait, should diagnoses be exact strings? Yes, "G47.33", "E66.9".
Medications: "sertraline".
All good.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 433 tokens
The user wants to extract specific information from a handwritten referral document.
The required fields are:
- `diagnoses`: ICD-10-CM diagnosis codes literally in the document.
- `medications`: Current medications, lower-case generic names, no dose/route/frequency.
- `referring_npi`: 10-digit NPI of the referring provider.
- `dates_of_service`: Dates of service requested/performed.
- `allergies`: Allergies/intolerances, lower-case, substance only.
Document content:
"Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38.
Diagnoses: G47.33, E66.9
She takes sertraline 100 mg daily.
No allergies.
Thank you,
R. Delacroix, MD
Delacroix Family Practice, Garland TX"
Extraction:
- `diagnoses`: ["G47.33", "E66.9"]
- `medications`: ["sertraline"]
- `referring_npi`: [] (No NPI provided)
- `dates_of_service`: [] (No dates provided)
- `allergies`: [] ("No allergies")
Let's double check.
Diagnoses: G47.33, E66.9 -> both are ICD-10 codes.
Medications: sertraline -> generic, lowercase.
Referring NPI: none.
Dates of service: none.
Allergies: none.
Format: JSON array of strings for each key.
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
All good.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 431 tokens
The user wants to extract specific fields from a transcribed handwritten referral.
Fields to extract:
1. **diagnoses**: ICD-10-CM diagnosis codes literally in the document describing the member's own conditions.
* Document says: "Diagnoses: G47.33, E66.9"
* Extract: ["G47.33", "E66.9"]
2. **medications**: Current medications, lower-case generic names, no dose/route/frequency.
* Document says: "She takes sertraline 100 mg daily."
* Extract: ["sertraline"]
3. **referring_npi**: 10-digit NPI of the referring/ordering provider.
* Document says: "R. Delacroix, MD" but no NPI is provided.
* Extract: []
4. **dates_of_service**: Dates of service requested or performed.
* Document doesn't mention any specific dates of service.
* Extract: []
5. **allergies**: Allergies and intolerances, lower-case, substance only.
* Document says: "No allergies."
* Extract: []
Let's double check.
Diagnoses: G47.33, E66.9 -> ["G47.33", "E66.9"]
Medications: sertraline -> ["sertraline"]
Referring NPI: none -> []
Dates of service: none -> []
Allergies: none -> []
Format as JSON.
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
All good.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GLM-5.3 · Z.ai4.1s · 674 out | 3.3s · 745 out | 4.2s · 545 out
Reasoning · 618 tokens
Let me analyze this task carefully.
The document is a handwritten referral, transcribed. It's from Dr. R. Delacroix of Delacroix Family Practice, Garland TX, requesting a sleep study for a patient.
Required fields:
1. "diagnoses" — ICD-10-CM diagnosis codes that appear LITERALLY in the document. The document literally lists "G47.33" (obstructive sleep apnea) and "E66.9" (obesity, unspecified). These appear literally. Wait — but the instructions say "codes that appear LITERALLY in the document and describe the member's own conditions." G47.33 and E66.9 both appear literally. The clinical description: snoring heavily, witnessed apneas, BMI 38 — these are consistent with OSA and obesity. So both diagnoses should be included.
Hmm, but wait — is G47.33 appropriate here? The document literally lists it under "Diagnoses:". So yes, both G47.33 and E66.9.
2. "medications" — current medications, lower-case generic names, no dose/route/frequency. "She takes sertraline 100 mg daily." → "sertraline". No discontinuation mentioned.
3. "referring_npi" — NPI of the REFERRING or ORDERING provider. The document shows "R. Delacroix, MD" and "Delacroix Family Practice, Garland TX" but NO NPI number. So return an empty array.
4. "dates_of_service" — dates of service being requested or performed. The document is a referral for a sleep study but no date of service is given. No dates at all in the document. Return empty array.
5. "allergies" — "No allergies." → empty array. Actually, the document explicitly states no allergies, so empty array is appropriate.
So the answer:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Wait — should I double-check "referring_npi"? The task says "If no complete 10-digit NPI is present, return an empty array." There's no NPI in the document. Correct, empty.
Dates: no dates of service at all. Empty.
Diagnoses: both codes appear literally. G47.33 = obstructive sleep apnea; the narrative mentions witnessed apneas and snoring, BMI 38 — consistent with obesity. Both are the member's own conditions (listed under "Diagnoses:"). Not family history. Include both.
Medications: sertraline, lower-case generic. It's already generic. Include.
Allergies: "No allergies" — empty array.
That's the answer. Output exactly one fenced JSON block.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 678 tokens
Let me analyze this document carefully.
The document is a handwritten referral, transcribed. It's from Dr. R. Delacroix of Delacroix Family Practice, Garland TX, requesting a sleep study for a patient.
Fields to extract:
1. "diagnoses" - ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe the member's own conditions. The document literally lists "Diagnoses: G47.33, E66.9". G47.33 is obstructive sleep apnea, E66.9 is obesity unspecified. These appear literally and describe the member's own conditions. So: ["G47.33", "E66.9"]
2. "medications" - member's CURRENT medications, lower-case generic names, no dose/route/frequency. The document says "She takes sertraline 100 mg daily." So sertraline is current. Lower-case generic: ["sertraline"]
3. "referring_npi" - NPI of the REFERRING or ORDERING provider only, as a 10-digit string. The document shows no NPI at all. Dr. R. Delacroix is the referring provider but no NPI number is present. So: empty array [].
4. "dates_of_service" - dates of service being requested or performed, in YYYY-MM-DD. The document says "Please see my patient for a sleep study." No date of service is given. There's no date at all in the document (not even a document-written date). So: empty array [].
5. "allergies" - allergies and intolerances, lower-case, substance only. Document says "No allergies." So: empty array [].
So the answer:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Wait — should I consider whether "G47.33" and "E66.9" describe the member's own conditions? Yes — the referral is about the patient's snoring, witnessed apneas, and BMI 38. G47.33 is obstructive sleep apnea (the condition being evaluated), E66.9 is obesity (consistent with BMI 38). These are listed under "Diagnoses:" literally in the document. They describe the member's own conditions, not family history. So both should be included.
The task says "Do not assign a code for a condition the document names in words only." Snoring and witnessed apneas are named in words but we don't assign codes for them — the codes are literally given already. G47.33 does relate to apnea. But we only include literal codes. So we include G47.33 and E66.9.
Medications: sertraline 100 mg daily — current medication, generic name already. Lower-case: "sertraline".
No discontinued medications mentioned.
Referring NPI: none present. Empty array.
Dates of service: none. The sleep study is being requested but no date is given. Empty array.
Allergies: "No allergies" — empty array.
That's the answer.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 486 tokens
Let me work through this document extraction task.
The document is a handwritten referral, transcribed. Let me extract each field.
**diagnoses**: ICD-10-CM diagnosis codes that appear LITERALLY in the document. The document says "Diagnoses: G47.33, E66.9". So G47.33 (obstructive sleep apnea) and E66.9 (obesity, unspecified) appear literally. Both describe the member's own conditions (per the referring provider). Family history — none mentioned. So diagnoses = ["G47.33", "E66.9"].
**medications**: Current medications, lower-case generic names, no dose/route/frequency. Document says "She takes sertraline 100 mg daily." Sertraline is already generic. So medications = ["sertraline"]. No discontinued meds mentioned.
**referring_npi**: The National Provider Identifier of the REFERRING or ORDERING provider only, as a 10-digit string. The document is signed "R. Delacroix, MD, Delacroix Family Practice, Garland TX". No NPI is shown anywhere. So return empty array [].
**dates_of_service**: Dates of service being requested or performed, YYYY-MM-DD. The referral requests a sleep study but no date is given. No dates in the document at all. So empty array [].
**allergies**: "No allergies." So empty array []. The substance-only format applies; since there are no allergies, empty array.
Let me double-check the diagnoses rule: "ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe the member's own conditions. Do not assign a code for a condition the document names in words only." G47.33 and E66.9 appear literally. Both are the member's conditions (sleep apnea suspected, obesity). Include both.
Medications: sertraline — generic name already, lower-case, no dose. Good.
All output keys are arrays of strings. Empty arrays where not supplied.
Final JSON:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Qwen3.8 Max · Alibaba15.7s · 610 out | 8.6s · 271 out | 8.3s · 303 out
Reasoning · 527 tokens
We need answer user's task. Need extract fields from handwritten referral transcribed. Need output exactly one fenced JSON code block. Need analyze carefully.
Document:
--- HANDWRITTEN REFERRAL, TRANSCRIBED ---
To whom it may concern,
Please see my patient for a sleep study. She has been snoring heavily and her
husband reports witnessed apnoeas. BMI is 38.
Diagnoses: G47.33, E66.9
She takes sertraline 100 mg daily.
No allergies.
Thank you,
R. Delacroix, MD
Delacroix Family Practice, Garland TX
Required keys arrays strings:
diagnoses: ICD-10-CM codes literally appear and describe member's own conditions. G47.33, E66.9. Include both? G47.33 obstructive sleep apnea? E66.9 obesity unspecified. They appear under diagnoses. Yes.
medications: current medications lower-case generic names no dose route freq. "She takes sertraline 100 mg daily." Current. generic sertraline. ["sertraline"]
referring_npi: NPI of referring/ordering provider only. Document has R. Delacroix, MD but no NPI. Empty array.
dates_of_service: dates requested/performed. None. Sleep study requested but no date. Empty.
allergies: allergies/intolerances lower-case substance only. "No allergies." Means no allergies. Should return empty array? The key says allergies and intolerances, lower-case, substance only. If no allergies, empty. Do not put "no allergies" because substance only. Empty array.
Need ensure JSON only. Possibly arrays empty. final:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Double-check: Diagnoses maybe G47.33 and E66.9. Are they member's own conditions? Yes. No family history. No procedure codes. Medication: brand? sertraline generic already. Lower-case. No dose.
Referring NPI: no 10-digit. Dates: none. Allergies: no allergies empty. Good.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 188 tokens
We need answer user's request. Need produce final JSON only. Need extract from handwritten referral transcribed.
Fields:
diagnoses: ICD-10-CM codes literally in doc describe member's conditions. Includes G47.33, E66.9. Yes.
medications: current meds lower-case generic no dose. "She takes sertraline 100 mg daily." current, generic sertraline. Include ["sertraline"].
referring_npi: NPI of referring/ordering provider only. Document has R. Delacroix, MD but no NPI. empty [].
dates_of_service: requested sleep study, no date. empty [].
allergies: "No allergies." Should array empty? It says allergies and intolerances substance only. If no allergies, empty. Not include "no allergies". empty.
Need ensure JSON code block only.
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 220 tokens
We need answer user's task. Need produce JSON only. Need extract from handwritten referral.
Need required keys arrays strings:
diagnoses: ICD-10-CM codes literally in doc describing member's conditions. Document: Diagnoses: G47.33, E66.9. Include both? Yes literal. Not family history. OK.
medications: current meds lower-case generic names no dose. "She takes sertraline 100 mg daily." current, generic sertraline. Include ["sertraline"].
referring_npi: NPI of referring/ordering provider only. Doc signed R. Delacroix, MD no NPI. empty [].
dates_of_service: dates requested/performed. None. Sleep study requested but no date. empty.
allergies: allergies/intolerances lower-case substance only. "No allergies." So empty []? Usually if no allergies, empty array. Not include "no allergies" because substance only. empty.
Need final exactly fenced JSON. Ensure no extra keys.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GPT-5.6 Terra · OpenAI1.7s · 77 out | 1.5s · 89 out | 2.0s · 86 out
json{"diagnoses":["G47.33","E66.9"],"medications":["sertraline"],"referring_npi":[],"dates_of_service":[],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GLM-5.3 Flash · Z.ai16.7s · 279 out | 8.7s · 367 out | 9.6s · 437 out
Reasoning · 231 tokens
The task: extract fields from a handwritten referral transcript.
Diagnoses: G47.33 (obstructive sleep apnea), E66.9 (obesity, unspecified). Both appear literally. Good.
Medications: sertraline 100 mg daily → "sertraline" (lower-case generic, no dose). Current. Good.
Referring NPI: The document shows "R. Delacroix, MD, Delacroix Family Practice, Garland TX" — no NPI at all. So empty array.
Dates of service: No dates mentioned anywhere. The document was transcribed but no date written. Empty array.
Allergies: "No allergies." → no allergies reported, so empty array (NKDA is not an allergy). The instruction: "allergies and intolerances, lower-case, substance only" — empty where the document does not supply the value. "No allergies" means no allergy value supplied. Empty array.
Output: one fenced JSON block with keys: diagnoses, medications, referring_npi, dates_of_service, allergies.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 308 tokens
Let me analyze this handwritten referral transcription.
Required fields:
1. "diagnoses" - ICD-10-CM codes that appear LITERALLY. G47.33 (obstructive sleep apnea) and E66.9 (obesity, unspecified) appear literally. Both describe the member's own conditions. So ["G47.33", "E66.9"].
2. "medications" - current medications, lower-case generic names, no dose. "She takes sertraline 100 mg daily." So ["sertraline"]. Not discontinued.
3. "referring_npi" - NPI of referring/ordering provider as 10-digit string. The document says "R. Delacroix, MD, Delacroix Family Practice, Garland TX" — no NPI present. Return empty array.
4. "dates_of_service" - dates of service being requested or performed. "Please see my patient for a sleep study" — no date given. No dates in document. The document date isn't given either. Empty array.
5. "allergies" - "No allergies." So empty array. NKDA means no known drug allergies — document says "No allergies" so empty array.
Wait — should BMI 38 be considered? The instruction says do not assign a code for a condition the document names in words only. BMI 38 is named but E66.9 is already listed literally. So only the literal codes.
Output the JSON block only.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 378 tokens
Let me analyze this handwritten referral for the Document Extraction task.
Required keys:
1. "diagnoses" - ICD-10-CM codes that appear LITERALLY in the document describing the member's own conditions. The document lists "G47.33, E66.9". G47.33 is obstructive sleep apnea, E66.9 is obesity unspecified. Both appear literally and describe the patient's own conditions. So diagnoses: ["G47.33", "E66.9"].
2. "medications" - current medications as lower-case generic names, no dose/route/frequency. "She takes sertraline 100 mg daily." So sertraline is current. Generic name is sertraline (it's already generic). medications: ["sertraline"].
3. "referring_npi" - NPI of referring/ordering provider as 10-digit string. The document shows "R. Delacroix, MD, Delacroix Family Practice, Garland TX" but no NPI at all. So empty array: [].
4. "dates_of_service" - dates of service being requested or performed. The referral requests a sleep study but no date is given. Empty array: [].
5. "allergies" - allergies, lower-case, substance only, no reaction. "No allergies." So the document explicitly states no allergies — that means the allergy list is empty. Empty array: [].
Also check: BMI 38 — that's a finding, not a diagnosis code; E66.9 covers obesity. Do not assign codes for conditions named in words only — snoring, witnessed apnoeas are symptoms described in words; the codes given are literal. So keep only G47.33 and E66.9.
Output exactly one fenced JSON code block.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Hunyuan 4 · Tencent60.8s · 3,357 out | 25.2s · 1,443 out | 26.2s · 1,575 out
Reasoning · 3,289 tokens
We need answer as JSON code block only. Need extract fields from document.
Document: handwritten referral transcribed. Request: sleep study. Patient has snoring, witnessed apneas, BMI 38. Diagnoses: G47.33, E66.9. Takes sertraline 100 mg daily. No allergies. Referring/ordering provider: R. Delacroix, MD, Delacroix Family Practice, Garland TX. But referring_npi required: National Provider Identifier of the REFERRING or ORDERING provider only, as 10-digit string. If document shows servicing or rendering NPI as well, don't include. If no complete 10-digit NPI present, return empty array. Document has no NPI anywhere. The signature is R. Delacroix, MD but no NPI number. So referring_npi should be empty array [].
Now required keys:
- diagnoses: ICD-10-CM diagnosis codes that appear LITERALLY in document and describe member's own conditions. "G47.33" appears literally. "E66.9" appears literally. Both describe member's conditions: obstructive sleep apnea (G47.33) and obesity, unspecified (E66.9). Not family history. Include as strings exactly? They appear with comma and space: "G47.33, E66.9". Need include codes. The format in document: G47.33, E66.9. Should we output "G47.33" and "E66.9". Yes. Need ensure lower-casing/trim for set comparison; codes upper maybe? The instruction: "Scoring is a set comparison after lower-casing and trimming." So casing doesn't matter, but output strings maybe as they appear? Usually exact literal? The question: "ICD-10-CM diagnosis codes that appear LITERALLY in the document". So include codes as they appear? They appear as G47.33 and E66.9. The array of strings: ["G47.33", "E66.9"] or maybe ["G47.33, E66.9"]? No, it says diagnosis codes (plural) that appear literally. Each code is a code. Extract fields; values are codes. So array items should be "G47.33" and "E66.9". Since lower-casing scoring, fine.
- medications: member's CURRENT medications, as lower-case generic names with no dose, route, frequency. Convert brand to generic. Omit discontinued/stopped. Document: "She takes sertraline 100 mg daily." Sertraline is generic already (brand Zoloft). Lower-case generic: "sertraline". No dose/route/freq. Output ["sertraline"]. Need check if any other meds? No.
- referring_npi: no NPI. Return [].
- dates_of_service: dates of service being requested or performed, in YYYY-MM-DD. Do not include dates anticipated, not yet booked, or outside this request. Do not include date document written unless also date of service. Document has no dates at all! There is no date of service, no written date. So empty array []. Wait: It says "Please see my patient for a sleep study." No date. There is no date. So [].
- allergies: allergies and intolerances, lower-case, substance only, with no reaction description. Document: "No allergies." That means no known allergies. Required key allergies: "allergies and intolerances, lower-case, substance only". If document says no allergies, what to output? Empty array? We need interpret: Extract fields below and nothing else. "allergies" is required key, array of strings, empty where the document does not supply the value. Document says "No allergies." Does that supply a value? It supplies negative, but the field asks for allergies and intolerances. Usually if patient has no allergies, the extracted value is an empty list (no substances). However, some extraction tasks might expect ["no allergies"]? But instruction: "substance only, with no reaction description." "No allergies" is not a substance; it's a statement of absence. The required value is array of strings, empty where document does not supply the value. Since there are no allergies (no substances), should we output [] or ["no allergies"]? Let's parse carefully.
Key: "allergies" — allergies and intolerances, lower-case, substance only, with no reaction description. Example: if document says "Penicillin - rash", substance is penicillin (lower-case). If document says "No allergies", there are no substances. The gold set for allergies likely empty array because no allergy substances. The instruction: "empty where the document does not supply the value". The document does supply the value that there are no allergies, but the value for the field (list of substances) is empty. In similar extraction tasks, "No allergies" results in empty list for allergies (no allergy substances). It is not a medication list where you might list "none". Let's think: If required key is "allergies" and array of strings. If member has no allergies, you would output []. Because the field is the list of allergies; none exist. Not output ["no allergies"] because that's not a substance, and scoring set would not have that. They said "A value you add that is not in the gold set costs you exactly as much as a value you miss." If gold set is empty and we output ["no allergies"], that's a false positive and costs. If gold set is ["no allergies"] and we output [], we miss value. Which is correct? Need infer from phrasing: "allergies and intolerances, lower-case, substance only, with no reaction description." They explicitly say substance only. "No allergies" is not a substance. So do not include it. Thus empty array. Yes.
Let's double-check other fields.
Diagnoses: The document lists: "Diagnoses: G47.33, E66.9". These appear literally. Are they member's own conditions? Yes: patient has sleep study for snoring/apnea; G47.33 is obstructive sleep apnea (adult) (pediatric?). Actually G47.33 is Obstructive sleep apnea (adult) (pediatric?) Let's recall: G47.33 is Obstructive sleep apnea (adult) (child?) Wait ICD-10: G47.33 is Obstructive sleep apnea (adult) (pediatric?). Actually G47.30 is unspecified sleep apnea, G47.31 is idiopathic, G47.32 is obstructive sleep apnea (adult) (pediatric?) Wait: Let's check: G47.30 Unspecified sleep apnea; G47.31 Primary central sleep apnea; G47.32 High altitude periodic breathing; G47.33 Obstructive sleep apnea (adult) (pediatric?) Hmm maybe G47.33 is obstructive sleep apnea (adult) (pediatric)? Actually in ICD-10-CM, G47.33 is Obstructive sleep apnea (adult) (pediatric?) Let me recall: G47.33 - Obstructive sleep apnea (adult) (pediatric)?? Wait there is G47.33 for Obstructive sleep apnea (adult) (pediatric?) No, I think G47.33 is "Obstructive sleep apnea (adult) (pediatric)"? Let's verify from memory: In ICD-10, G47.3 is Sleep apnea. G47.30 Unspecified sleep apnea. G47.31 Primary central sleep apnea. G47.32 High altitude periodic breathing. G47.33 Obstructive sleep apnea (adult) (pediatric). Actually I recall G47.33 is obstructive sleep apnea (adult) (pediatric?) There is also G47.33 for "Obstructive sleep apnea (adult) (pediatric)"? Wait, I've seen G47.33 for obstructive sleep apnea (adult) (pediatric) and G47.33? Let's not worry; the document says G47.33. It's an ICD-10-CM code. E66.9 is Obesity, unspecified. Both are member conditions. The referral says BMI 38 (obesity). Yes.
Medications: sertraline. Lower-case generic: "sertraline". Brand name? Sertraline is generic; brand Zoloft, but they wrote sertraline, which is generic. So output "sertraline". No dose etc.
Referring NPI: Does "R. Delacroix, MD" count as provider but no NPI. The key says: "the National Provider Identifier of the REFERRING or ORDERING provider only, as a 10-digit string. If the document shows a servicing or rendering NPI as well, do not include it. If no complete 10-digit NPI is present, return an empty array." Indeed no 10-digit NPI. So [].
Dates of service: The document is a referral for a sleep study. There is no date of service, no date of request. No dates at all. So [].
Allergies: "No allergies." So [].
Need output exactly one fenced JSON code block and nothing else. No preamble, no commentary. Required JSON keys: diagnoses, medications, referring_npi, dates_of_service, allergies. Every one an array of strings, empty where document does not supply value.
Let's construct JSON:
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Wait, check date format: dates_of_service dates in YYYY-MM-DD. None. Empty array.
Check "referring_npi" example: If no complete 10-digit NPI is present, return an empty array. Yes. So [].
One nuance: "Diagnoses: G47.33, E66.9" The codes are separated by a comma and space. If we output "G47.33" and "E66.9", that's correct. Should we maintain literal with comma? No: "ICD-10-CM diagnosis codes that appear LITERALLY in the document". Each code appears literally. The string "G47.33" appears literally. The string "E66.9" appears literally. The string "G47.33, E66.9" also appears but that's two codes with separator. The field asks for codes, array of strings. So each element is a code. Yes.
Check medications: "She takes sertraline 100 mg daily." Lower-case generic, no dose, route, frequency. "sertraline" is generic. Output ["sertraline"].
Allergies: If they wanted NKDA or "no allergies" maybe? Let's examine wording: "allergies and intolerances, lower-case, substance only, with no reaction description." If the document says "No allergies.", the substance only extraction yields no substances. Thus []. The instruction: "empty where the document does not supply the value". The document supplies the value "no allergies", but the value expected is list of substances. If they wanted to record "no allergies", they'd say include "no known allergies"? But they say substance only, so "no allergies" is not a substance. In many similar document extraction tasks from this harness (maybe previous items), for allergies they output [] when document says "No allergies" or "NKDA". Let's be confident.
But wait: There is a possibility they expect ["no allergies"] because they ask for allergies and intolerances, and if none, the document supplies "no allergies" as the value? Let's read: Required JSON keys, every one an array of strings, empty where the document does not supply the value. The key "allergies" is described as "allergies and intolerances, lower-case, substance only, with no reaction description." This defines the format of the strings in the array: they must be lower-case substance only. If the document had an allergy, e.g., "Penicillin - rash", you extract "penicillin". If the document says "No allergies", there is no substance, so no string can satisfy "substance only". Therefore the array is empty. Yes.
Now, is there any chance "G47.33" describes a condition named in words only? The document says: "Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38. Diagnoses: G47.33, E66.9". The codes appear literally. For G47.33, they didn't write "obstructive sleep apnea" in words, but the code is listed. It's a code that appears literally. We include it. E66.9 appears literally, corresponds to obesity, BMI 38. Include.
Check if any procedure/supply/HCPCS codes? No. G47.33 and E66.9 are ICD-10-CM (start with letter, with decimal). Yes.
Check referring provider NPI: Could "R. Delacroix, MD" have an NPI? No. There is no 10-digit number. The address "Garland TX" no zip? No. So [].
Check dates: There is no date. The transcription header: "--- HANDWRITTEN REFERRAL, TRANSCRIBED ---" no date. So [].
Let's ensure JSON syntax: use double quotes. Fenced code block with json. Exactly:
```json
{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Or inline arrays. Both fine. Need no trailing commas. Must be exactly one fenced JSON code block and nothing else. The example output contract shows:
```json
{ ... }
```
So I will output the fenced block. Ensure no text before/after.
Let's verify keys: all five required. "diagnoses", "medications", "referring_npi", "dates_of_service", "allergies". Yes.
One more nuance: The task says: "Refer to Item ABS-006." Nothing else.
Let's consider medication: "sertraline 100 mg daily" -> lower-case generic: "sertraline". It is already generic. No brand conversion needed. If they wrote Zoloft, we would output sertraline. Good.
Consider "Diagnoses: G47.33, E66.9" - is E66.9 a diagnosis code that describes the member's own condition? Yes, obesity. Document says BMI is 38, which supports obesity. It's not family history. Good.
Now, check if "G47.33" is actually a code for obstructive sleep apnea. Yes. The patient is referred for sleep study due to snoring and witnessed apneas. Good.
Should "witnessed apnoeas" be assigned a code? The instruction: "Do not assign a code for a condition the document names in words only." The document names snoring and witnessed apneas in words only, but it also gives a code G47.33 (obstructive sleep apnea) in the Diagnoses line. So we don't assign a code for snoring or apneas; we just include the literal codes. Good.
Now final answer.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 1,385 tokens
We need answer with JSON only. We have document: handwritten referral transcribed. Need extract fields:
Required keys:
"diagnoses": ICD-10-CM diagnosis codes that appear LITERALLY in document and describe member's own conditions. Do not assign a code for condition named in words only. Do not include procedure/supply/HCPCS. Do not include codes listed under family history. Document has "Diagnoses: G47.33, E66.9". These are ICD-10-CM codes. They describe member's own conditions? Yes: G47.33 obstructive sleep apnea (adult) (pediatric?) Actually G47.33 is obstructive sleep apnea, adult/pediatric? Wait: G47.33 is obstructive sleep apnea (adult) (pediatric?) Let's check: ICD-10-CM: G47.33 Obstructive sleep apnea (adult) (pediatric?) Actually G47.30 is obstructive sleep apnea, unspecified; G47.31 idiopathic; G47.32 obstructive sleep apnea, pediatric; G47.33 obstructive sleep apnea, adult. Yes. E66.9 obesity, unspecified. BMI 38 corresponds obesity. No family history. So diagnoses array: ["G47.33", "E66.9"].
"medications": CURRENT medications, lower-case generic names, no dose/route/frequency. Convert brand to generic. Omit discontinued/stopped. Document: "She takes sertraline 100 mg daily." Sertraline is generic (brand Zoloft). Lower-case: "sertraline". No other meds. So ["sertraline"].
"referring_npi": NPI of referring or ordering provider only, 10-digit string. Document shows "R. Delacroix, MD" and "Delacroix Family Practice, Garland TX". No NPI number. It says "referring_npi" array of strings. If no complete 10-digit NPI present, return empty array. There is none. So [].
"dates_of_service": dates of service being requested or performed, YYYY-MM-DD. Document: referral for sleep study. No date of service given. It says "Please see my patient for a sleep study." No appointment date. No anticipated? There is no date at all, not even document written date. The transcribed header no date. So empty array [].
"allergies": allergies and intolerances, lower-case, substance only, no reaction. Document: "No allergies." So empty array? If no allergies known, document says No allergies. The field asks allergies and intolerances. If document states no allergies, then there are no allergy substances to list. Should it be empty array or ["no allergies"]? The spec: "allergies" allergies and intolerances, lower-case, substance only, with no reaction description. Scoring is set comparison. The gold likely empty array because no allergies. The instruction: arrays of strings, empty where document does not supply the value. Document says "No allergies." Does it supply the value? It supplies a negative, no substances. The field is for allergies/intolerances. Since none, empty array. Not include "no allergies" as substance. So [].
Let's double-check: The task family "DOCUMENT EXTRACTION". Required JSON keys, every one an array of strings, empty where document does not supply the value. It says empty where the document does not supply the value. Does document supply allergies? It supplies "No allergies" meaning no allergy substances. So empty array. Similarly referring NPI not supplied -> empty. Dates not supplied -> empty.
Need output exactly one fenced JSON code block and nothing else. Use JSON object with keys. Need ensure arrays of strings. For empty arrays, use [].
Let's verify no hidden things: Document header "--- HANDWRITTEN REFERRAL, TRANSCRIBED ---". It says "To whom it may concern, Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38. Diagnoses: G47.33, E66.9 She takes sertraline 100 mg daily. No allergies. Thank you, R. Delacroix, MD Delacroix Family Practice, Garland TX"
Diagnoses literally: G47.33, E66.9. Yes.
Medications: sertraline. Lower-case generic names with no dose etc. sertraline is generic. Good.
Allergies: No allergies. So [].
Referring NPI: The referring provider is R. Delacroix, MD? Document is a referral from Dr. Delacroix? It's a referral letter: "Please see my patient..." signed by R. Delacroix, MD, Delacroix Family Practice. This provider is the referring/ordering provider. But no NPI. So empty.
Dates: none. The sleep study is requested, not booked; no date. No date document written. So [].
Need output:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Check ordering: The required keys order doesn't matter? They asked include every required key. Probably in any order, but follow listed: diagnoses, medications, referring_npi, dates_of_service, allergies.
One nuance: In "diagnoses", do we include E66.9? Yes, it's an ICD-10-CM code appearing literally. The diagnoses line says "Diagnoses: G47.33, E66.9". They describe member's own conditions. Yes.
Another nuance: G47.33 is obstructive sleep apnea, adult. The patient is "She". Adult. Good.
Medication: "sertraline 100 mg daily". Generic name is sertraline. Lower-case. Good.
Allergies: "No allergies." There's no substance. Empty array. The instruction: "empty where the document does not supply the value". For allergies, does document supply a value? It supplies "No allergies" but the required value is substances. Since none, empty. The gold answer likely empty. We shouldn't put ["no allergies"] because that is not a substance and not lower-case maybe. It says substance only. So empty.
Need no trailing text, no commentary. Exactly fenced JSON code block. Use ```json ... ```.
Let's ensure JSON valid: double quotes, commas. Empty arrays.
Final answer.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 1,516 tokens
We need answer with JSON only. Need extract fields from document. Let's parse document.
Document: Handwritten referral transcribed.
"Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38.
Diagnoses: G47.33, E66.9
She takes sertraline 100 mg daily.
No allergies.
Thank you,
R. Delacroix, MD
Delacroix Family Practice, Garland TX"
Required keys:
- diagnoses: ICD-10-CM diagnosis codes that appear LITERALLY in document and describe member's own conditions. Do not assign code for condition named in words only. Do not include procedure/supply/HCPCS. Do not include codes listed under family history.
Document diagnoses: G47.33, E66.9. Both literal ICD-10-CM. G47.33 is obstructive sleep apnea (adult/pediatric?) Actually G47.33 is obstructive sleep apnea (adult) (pediatric?). E66.9 is obesity, unspecified. These appear literally and describe member's own conditions (the patient has snoring, witnessed apnoeas, BMI 38). Yes. So diagnoses array: ["G47.33", "E66.9"].
- medications: member's CURRENT medications, lower-case generic names with no dose/route/frequency. Convert brand to generic. Omit discontinued/stopped. Document: "She takes sertraline 100 mg daily." Sertraline is generic? Sertraline is generic name; brand is Zoloft. It says sertraline, which is already generic lower-case? Need lower-case: "sertraline". No dose. So ["sertraline"].
- referring_npi: NPI of REFERRING or ORDERING provider only, 10-digit string. If document shows servicing/rendering NPI as well, do not include. If no complete 10-digit NPI present, return empty array. Document: signed "R. Delacroix, MD" with practice "Delacroix Family Practice, Garland TX". No NPI number given. So empty array: [].
- dates_of_service: dates of service being requested or performed, YYYY-MM-DD. Do not include dates document describes as anticipated, not yet booked, or outside this request. Do not include date document was written unless it is also date of service. Document has no dates at all. No date written, no DOS. So empty array: [].
- allergies: allergies and intolerances, lower-case, substance only, with no reaction description. Document: "No allergies." So empty array? Should we output [] for no allergies? The task: "allergies" required key, array of strings, empty where document does not supply value. Document says "No allergies." Does that supply value? It supplies that there are no allergies. The instruction: empty where the document does not supply the value. The document does supply the value: no allergies (i.e., none). The gold set likely empty array. Wait, the key is allergies and intolerances... "No allergies." Means no known allergies. In previous similar tasks, if document says "No allergies" or "NKDA", the allergies array is empty []. Because there are no allergy substances. The instruction: "empty where the document does not supply the value" but here value is none, so empty array. Yes.
Need output exactly one fenced JSON code block and nothing else. Required keys: diagnoses, medications, referring_npi, dates_of_service, allergies. All arrays of strings. Use lower-case for medications and allergies. Diagnoses codes maybe uppercase as appear literally? Scoring set comparison after lower-casing and trimming. So case doesn't matter? It says lower-casing for set comparison, but we can output as literal: "G47.33", "E66.9". For medications lower-case: "sertraline". Allergies empty.
Need ensure no extra keys. Let's craft JSON:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Wait, check if "referring_npi" maybe should include something? No NPI. Document says "R. Delacroix, MD" but no NPI. So empty array.
Check dates_of_service: The referral is for a sleep study. "Please see my patient for a sleep study." No date. No date of service. So empty.
Check diagnoses: She has been snoring heavily and husband reports witnessed apnoeas. BMI is 38. Diagnoses: G47.33, E66.9. Do we need to assign code for obesity? E66.9 is obesity, unspecified, literal. Yes. G47.33 is obstructive sleep apnea, literal. No family history. Good.
Medications: "sertraline 100 mg daily" -> generic sertraline. Lower-case with no dose: "sertraline". It is current: "She takes". Not discontinued. Good.
Allergies: "No allergies." Lower-case substance only. None. Empty array.
Need final answer exactly fenced JSON code block. No preamble. Let's ensure fenced with ```json ... ```. Inside JSON, strings double-quoted. Arrays empty.
Let's think about if dates_of_service might include the date the document was written? There's no date written. So no.
Could there be an NPI hidden? "Delacroix Family Practice, Garland TX" no. "R. Delacroix, MD" no digits. So [].
One nuance: The output contract says: "Reply with exactly one fenced JSON code block and nothing else. No preamble, no commentary before or after, no second code block." And the example:
```json
{ ... }
```
So final answer should be:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Wait, the instruction: "Required JSON keys, every one an array of strings, empty where the document does not supply the value". For allergies, document says "No allergies." That's supplying a negative value. In similar extraction tasks, for "no allergies", the array is empty. Yes.
Let's double-check medication conversion: sertraline is generic. The brand name Zoloft is not mentioned. The document says "sertraline", which is the generic name. We output lower-case generic: "sertraline". Good.
Diagnoses: Should we output "G47.33" and "E66.9". The set comparison lower-cases and trims. G47.33 lower-case is g47.33. We can output as uppercase; fine.
Referring NPI: 10-digit string; empty array because none. Good.
Dates_of_service: empty array.
All good. Need not include null. Use empty arrays.
Let's produce final.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Qwen3.8 Flash · Alibaba3.3s · 262 out | 4.2s · 426 out | 2.9s · 215 out
Reasoning · 179 tokens
We need answer JSON only. Need extract fields. Need analyze document: handwritten referral. Diagnoses: ICD-10-CM codes appear literally: G47.33, E66.9. They describe member's conditions? sleep apnea and obesity. Include. No procedure/HCPCS. Family history none. Medications: current medication lower-case generic no dose: sertraline. Referring NPI: none. Dates of service: none explicit. It says see patient for sleep study but no date. Allergies: No allergies -> empty array.
Need output fenced JSON only. Keys arrays. Ensure lower-case diagnoses? Scoring lowercases/trimming. Use strings. Could diagnoses include codes as given? Use "g47.33", "e66.9" likely okay. medication "sertraline". dates empty.
json{
"diagnoses": [
"g47.33",
"e66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"g47.33",
"e66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 343 tokens
We need answer user's request. Need extract from document. Need output exactly one fenced JSON block and nothing else. Let's reason carefully.
Task: ABS-006 document extraction.
Document:
"--- HANDWRITTEN REFERRAL, TRANSCRIBED ---
To whom it may concern,
Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38.
Diagnoses: G47.33, E66.9
She takes sertraline 100 mg daily.
No allergies.
Thank you,
R. Delacroix, MD
Delacroix Family Practice, Garland TX"
Required keys arrays strings.
diagnoses: ICD-10-CM diagnosis codes appear literally and describe member's own conditions. G47.33, E66.9. Are these codes? Yes. Include.
medications: current meds lower-case generic no dose route frequency. sertraline 100 mg daily -> sertraline.
referring_npi: NPI of referring/ordering provider only 10-digit. None present. empty.
dates_of_service: dates of service requested/performed in YYYY-MM-DD. No dates. sleep study is requested but no date. anticipated? "Please see my patient for a sleep study" could be current request but no specific date. Do not include anticipated not yet booked. No complete date. empty.
allergies: allergies/intolerances lower-case substance only. "No allergies." -> empty.
Need JSON only.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 132 tokens
We need answer document extraction. Need parse referral. Required keys diagnoses, medications, referring_npi, dates_of_service, allergies.
Document: Diagnoses: G47.33, E66.9. These are ICD-10-CM literal. Member's conditions sleep apnea unspecified? obesity unspecified. Include.
Medications current: sertraline lower-case generic no dose.
Referring NPI none, empty.
Dates of service none; sleep study requested but no date, empty.
Allergies: No allergies -> empty.
Need output exactly JSON fenced block. No extra. Ensure arrays strings.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Kimi K2.6 · Moonshot3.8s · 901 out | 7.7s · 965 out | 5.8s · 1,179 out
The user wants me to extract specific fields from a transcribed handwritten referral document and output them as a JSON object with specific keys.
Let me analyze the document:
1. "diagnoses": ICD-10-CM diagnosis codes that appear literally in the document and describe the member's own conditions. Not family history.
- The document lists: G47.33, E66.9
- G47.33 is Obstructive sleep apnea
- E66.9 is Obesity, unspecified
- These appear literally, so include them.
2. "medications": Current medications, lower-case generic names, no dose/route/frequency. Convert brand to generic. Omit discontinued/stopped.
- "sertraline 100 mg daily" -> generic is sertraline (it's already generic), lower-case: "sertraline"
- No mention of discontinuation.
3. "referring_npi": National Provider Identifier of the REFERRING or ORDERING provider only, as a 10-digit string. Do not include servicing/rendering NPI. If no complete 10-digit NPI present, return empty array.
- The document says: "R. Delacroix, MD" and "Delacroix Family Practice, Garland TX"
- There is no NPI number provided. So empty array.
4. "dates_of_service": Dates of service being requested or performed, in YYYY-MM-DD. Do not include anticipated/not yet booked/outside this request. Do not include document date unless it's also date of service.
- The document is requesting a sleep study but doesn't specify a date. No dates are mentioned. So empty array.
5. "allergies": Allergies and intolerances, lower-case, substance only, no reaction description.
- "No allergies." -> empty array.
All values should be arrays of strings.
diagnoses: ["G47.33", "E66.9"] (order doesn't matter, but I'll keep as found)
medications: ["sertraline"]
referring_npi: [] (empty array)
dates_of_service: [] (empty array)
allergies: [] (empty array)
Wait, I need to check if there are any tricks. The document says "Please see my patient for a sleep study." This is a referral. No date of service for the sleep study is given. No NPI is given.
Let me double check the ICD-10 codes:
- G47.33: Obstructive sleep apnea (adult) (pediatric) - valid ICD-10-CM
- E66.9: Obesity, unspecified - valid ICD-10-CM
They appear literally. So include them.
Medications: "sertraline" - yes, generic.
Allergies: "No allergies." -> empty array.
Let me make sure about "referring_npi". The instruction says: "the National Provider Identifier of the REFERRING or ORDERING provider only, as a 10-digit string. If the document shows a servicing or rendering NPI as well, do not include it. If no complete 10-digit NPI is present, return an empty array."
There is no NPI at all in the document. So [].
dates_of_service: The document is a referral for a sleep study, but no date is specified. So [].
Final JSON:
json {
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Wait, I should check if "sertraline" is indeed the generic name. Yes, Zoloft is the brand name, sertraline is generic.
Also, the task says "Required JSON keys, every one an array of strings, empty where the document does not supply the value"
So empty arrays are correct for no value.
I will output exactly one fenced JSON code block with nothing else.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}The user wants a JSON extraction from a transcribed handwritten referral. Let me carefully parse the document against the required keys.
Required keys (all arrays of strings):
- "diagnoses": ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe the member's own conditions. Do NOT assign a code for a condition named in words only. Do NOT include procedure, supply, or HCPCS codes. Do NOT include codes listed under family history.
- "medications": member's CURRENT medications, as lower-case generic names with no dose, route, or frequency. Convert brand name to generic name. Omit discontinued/stopped meds.
- "referring_npi": NPI of the REFERRING or ORDERING provider only, as a 10-digit string. If document shows a servicing/rendering NPI, don't include it. If no complete 10-digit NPI present, return empty array.
- "dates_of_service": dates of service being requested or performed, in YYYY-MM-DD. Do not include anticipated/not yet booked/outside request dates. Do not include date document was written unless it's also the date of service.
- "allergies": allergies and intolerances, lower-case, substance only, no reaction description.
Let's go through the text:
"To whom it may concern, Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38."
Diagnoses: G47.33, E66.9
- G47.33 is Obstructive sleep apnea. E66.9 is Obesity, unspecified. These appear literally. So diagnoses = ["G47.33", "E66.9"]
Medications: "She takes sertraline 100 mg daily."
- Current medication: sertraline. Generic name is sertraline. Lower-case, no dose. So medications = ["sertraline"]
Referring NPI: The document mentions "R. Delacroix, MD, Delacroix Family Practice, Garland TX". There is no 10-digit NPI number provided. So referring_npi = [].
Dates of service: The referral says "Please see my patient for a sleep study." It doesn't specify a date for the sleep study, nor does it have any dates at all. So dates_of_service = [].
Allergies: "No allergies." So allergies = [].
Wait, the instruction says "allergies and intolerances, lower-case, substance only". If it says "No allergies", the array should be empty. So allergies = [].
Let me double-check everything.
Diagnoses: Literally appear: "G47.33", "E66.9". Yes.
Medications: "sertraline 100 mg daily" -> current, generic sertraline, lower case, no dose etc. Yes.
referring_npi: No NPI present at all. Empty array. Yes.
dates_of_service: No dates in document. Empty array. Yes.
allergies: "No allergies" -> empty array. Yes.
One concern: Does the instruction imply I should include the referring provider's NPI if present? It's not present. So empty.
Another concern: G47.33 and E66.9 are indeed ICD-10-CM codes. They appear literally. I should not convert BMI 38 into E66.9 if it wasn't there, but it IS there literally. The text also says BMI is 38, but the literal code E66.9 is listed under Diagnoses. So include it.
Are there any other literal codes? No.
Medications: sertraline is generic. Lower-case. So "sertraline".
Dates: No date mentioned. So empty.
Allergies: "No allergies". So empty array.
Output format: Exactly one fenced JSON code block and nothing else.
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Wait, is sertraline current? "She takes sertraline 100 mg daily." Yes, current.
No NPI. Correct.
No dates. Correct.
No allergies. Correct.
Looks good.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 1,143 tokens
The user wants me to extract specific fields from a transcribed handwritten referral document. Let me analyze the document carefully.
Document content:
- "Please see my patient for a sleep study. She has been snoring heavily and her husband reports witnessed apnoeas. BMI is 38."
- "Diagnoses: G47.33, E66.9"
- "She takes sertraline 100 mg daily."
- "No allergies."
- "Thank you, R. Delacroix, MD Delacroix Family Practice, Garland TX"
Required JSON keys (all arrays of strings):
1. "diagnoses" - ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe the member's own conditions. Do not assign a code for a condition the document names in words only. Do not include procedure, supply, or HCPCS codes. Do not include codes listed under family history.
- Document literally has: G47.33, E66.9
- G47.33 is Obstructive sleep apnea (adult) (pediatric) - describes member's condition
- E66.9 is Obesity, unspecified - describes member's condition (BMI 38 supports this)
- Both appear literally. So: ["G47.33", "E66.9"]
2. "medications" - member's CURRENT medications, as lower-case generic names with no dose, route, or frequency. Convert brand name to generic name. Omit any medication the document says was discontinued or stopped.
- Document says: "sertraline 100 mg daily"
- Generic name: sertraline
- Current medication? Yes, "She takes sertraline..." implies current.
- Lower-case: sertraline
- So: ["sertraline"]
3. "referring_npi" - National Provider Identifier of the REFERRING or ORDERING provider only, as a 10-digit string. If the document shows a servicing or rendering NPI as well, do not include it. If no complete 10-digit NPI is present, return an empty array.
- Document shows: "R. Delacroix, MD" and practice name, but no NPI number at all.
- No complete 10-digit NPI present.
- So: []
4. "dates_of_service" - dates of service being requested or performed, in YYYY-MM-DD. Do not include dates that the document describes as anticipated, not yet booked, or outside this request. Do not include the date the document was written unless it is also the date of service.
- Document is a referral for a sleep study, but no specific date is mentioned for the service. No date of service is provided. The referral asks "Please see my patient for a sleep study" but doesn't state a specific date when this should happen or when it was performed.
- Wait, is there any date in the document? Let me re-read... No, there are no dates mentioned at all in the transcribed document.
- So: []
5. "allergies" - allergies and intolerances, lower-case, substance only, with no reaction description.
- Document says: "No allergies."
- This means the member has no allergies. So the array should be empty? Or should I include something? The instruction says "empty where the document does not supply the value" but here the document explicitly states "No allergies." This means the value supplied is that there are none. So the array should be empty [].
- Wait, let me think carefully. If the document says "No allergies", does that mean I return [] or ["none"] or something? The instruction says "allergies and intolerances, lower-case, substance only". Since there are no substances listed as allergies, and the document explicitly says none, I should return an empty array. The empty array represents that there are no known allergies. If I put ["no allergies"] that would be wrong because it's not a substance.
Let me double check:
- Diagnoses: G47.33, E66.9 → both literal in document, member's own conditions.
- Medications: sertraline → lower-case generic, no dose/route/frequency.
- Referring NPI: None present → []
- Dates of service: No dates in document → []
- Allergies: Document says "No allergies." → [] (empty array because there are no allergy substances listed)
Format: exactly one fenced JSON code block and nothing else.
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Wait, I need to be careful about the diagnoses. G47.33 is "Obstructive sleep apnea" - yes, ICD-10-CM. E66.9 is "Obesity, unspecified" - yes, ICD-10-CM. The document literally lists them. So these are correct.
Medications: sertraline. Brand name is Zoloft, but document gives generic. Lower-case: sertraline.
NPI: No 10-digit string present.
Dates: None present.
Allergies: Explicitly none.
This looks correct.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100DeepSeek V4 Pro · DeepSeek5.7s · 386 out | 5.9s · 306 out | 4.8s · 308 out
Reasoning · 329 tokens
We are asked to extract specific fields from the document. The document is a transcribed handwritten referral. Let's identify the required keys:
- "diagnoses": ICD-10-CM diagnosis codes that appear literally in the document describing the member's own conditions. The document says "Diagnoses: G47.33, E66.9". So those codes are literally present. G47.33 is Obstructive sleep apnea (adult) (pediatric), E66.9 is Obesity, unspecified. So we include both.
- "medications": current medications, lower-case generic names. The document says "She takes sertraline 100 mg daily." Sertraline is the generic name, so we include "sertraline". No brand name mentioned, so no conversion needed.
- "referring_npi": NPI of the referring or ordering provider. The document is signed by R. Delacroix, MD, Delacroix Family Practice. There is no NPI number provided. So empty array.
- "dates_of_service": dates of service being requested or performed. The referral is for a sleep study, but no specific date is given. So empty array.
- "allergies": allergies and intolerances, lower-case, substance only. The document says "No allergies." So empty array.
Thus, we have:
diagnoses: ["G47.33", "E66.9"]
medications: ["sertraline"]
referring_npi: []
dates_of_service: []
allergies: []
Note: Order doesn't matter, but I'll list them as they appear.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 248 tokens
We are asked to extract specific fields from this handwritten referral transcription. Let's examine each key.
The document is a referral for a sleep study. The patient has snoring, witnessed apneas, BMI 38. Diagnoses are explicitly given: "Diagnoses: G47.33, E66.9". Those are ICD-10-CM codes: G47.33 is obstructive sleep apnea, E66.9 is obesity, unspecified. So "diagnoses" array: ["G47.33", "E66.9"].
Medications: "She takes sertraline 100 mg daily." That's current, generic name is sertraline, already generic. So medications: ["sertraline"].
Referring NPI: The document says "R. Delacroix, MD / Delacroix Family Practice, Garland TX". No NPI is provided. So referring_npi is empty array.
Dates of service: No specific date of service mentioned. The referral is for a sleep study, but no date is provided. The document was presumably written but no date given either. So dates_of_service empty.
Allergies: "No allergies." So allergies empty array.
Thus output: all arrays as specified.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 242 tokens
We are asked to extract from this hand-written referral (transcribed) the following fields as arrays of strings:
- "diagnoses": ICD-10-CM diagnosis codes that appear literally in the document and describe the member's own conditions. Here: "G47.33" and "E66.9" are given. So we output both.
- "medications": current medications, as lower-case generic names. Sertraline is a generic name (Zoloft is brand). It is listed: "sertraline 100 mg daily." So we output "sertraline". No dose, route, frequency. So just "sertraline".
- "referring_npi": The NPI of the referring or ordering provider. The document says "R. Delacroix, MD" but no NPI is provided. So empty array.
- "dates_of_service": dates of service being requested or performed. The referral asks for a sleep study, but no specific date is given. It's a referral for a future service, not a date. So empty array.
- "allergies": "No allergies" so empty array.
Thus, the output.json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GPT-5.6 Luna · OpenAI1.5s · 77 out | 56.8s · 94 out | 3.7s · 84 out
json{"diagnoses":["G47.33","E66.9"],"medications":["sertraline"],"referring_npi":[],"dates_of_service":[],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{"diagnoses":["G47.33","E66.9"],"medications":["sertraline"],"referring_npi":[],"dates_of_service":[],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 35 tokens
**Extracting relevant info**
I need to extract specific information, but it looks like there are no NPI or date details provided. When it says "No allergies," that could mean there aren't any at all, so I'd note that. I also see there's mention of diagnoses which seem to refer to literal codes for specific conditions. Finally, I should pay attention to the medication mentioned, which is sertraline. Keeping all this organized is essential!json{"diagnoses":["G47.33","E66.9"],"medications":["sertraline"],"referring_npi":[],"dates_of_service":[],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100DeepSeek V4 Flash · DeepSeek12.7s · 316 out | 15.0s · 254 out | 2.8s · 341 out
Reasoning · 263 tokens
We need to extract diagnoses, medications, referring_npi, dates_of_service, allergies.
Document: referral letter. Diagnoses: G47.33, E66.9 (ICD-10 codes). Medications: sertraline (convert to generic? Sertraline is generic name, but note it's a brand? Sertraline is generic for Zoloft, but the text says "sertraline" so keep as is. Lowercase. No dose. So "sertraline". Allergies: "No allergies" -> empty. Referring NPI: none mentioned. Dates of service: none mentioned. So diagnoses: ["g47.33", "e66.9"]? Codes should be as written? Typically ICD-10 codes without decimal? The document says G47.33, E66.9. We need to output as they appear? The instruction says "ICD-10 diagnosis codes that appear LITERALLY in the document". So keep as "G47.33" and "E66.9". But note they are uppercase. We'll keep as written. Medications: "sertraline" (lowercase). Referring_npi: empty array. Dates_of_service: empty array. Allergies: empty array. Ensure JSON. {
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 196 tokens
We need answer extraction task. Need output JSON with arrays of strings. Need parse document.
We need be careful. Document is handwritten referral transcribed. Diagnoses: G47.33, E66.9 literally appear. These describe member's conditions? G47.33 obstructive sleep apnea? E66.9 obesity unspecified. They appear under Diagnoses. Include. medications: current medications lower-case generic names no dose. "She takes sertraline 100 mg daily." Sertraline is generic? Sertraline is generic name (Zoloft brand). Include "sertraline". referring_npi: The referring/ordering provider only. Document no NPI. Return empty array. dates_of_service: No dates? "Please see my patient for a sleep study" no date. No dates. allergies: "No allergies." So empty array.
Need output exactly one fenced JSON code block. Keys arrays of strings. Use JSON.
Let's produce.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 284 tokens
We need answer JSON only. Need extract fields. Let's parse document.
Handwritten referral transcribed. Diagnoses: G47.33, E66.9. These are ICD-10-CM codes literally in document. Need include in diagnoses array: ["G47.33", "E66.9"]? Need check? G47.33 is obstructive sleep apnea? E66.9 obesity unspecified. Yes.
Medications: "She takes sertraline 100 mg daily." Current medication. Lower-case generic name no dose/route/freq. sertraline. Brand? sertraline generic. Include "sertraline".
Referring NPI: referring/ordering provider only. Document shows no NPI. Return empty array.
Dates of service: none. Date document not given. Empty array.
Allergies: document says No allergies. allergies array empty. Substance only, no reaction. No allergies => empty.
Need output with arrays strings. Ensure keys. "diagnoses", "medications", "referring_npi", "dates_of_service", "allergies". All arrays.
Also note "Do not include procedure, supply, or HCPCS codes." None.
Should we include G47.33 and E66.9 exactly uppercase? Lower-casing/trimming in grader; order no matter. Could output uppercase as literal. Fine.
Need fenced JSON block only.json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Haiku 4.5 · Anthropic13.2s · 1,046 out | 10.3s · 731 out | 11.9s · 561 out
Reasoning · 978 tokens
Let me analyze this handwritten referral document carefully according to the task requirements.
I need to extract:
1. **diagnoses**: ICD-10-CM diagnosis codes that appear LITERALLY in the document
2. **medications**: current medications as lower-case generic names with no dose, route, or frequency
3. **referring_npi**: the NPI of the referring/ordering provider as a 10-digit string
4. **dates_of_service**: dates of service being requested or performed
5. **allergies**: allergies and intolerances, lower-case, substance only
Let me go through the document:
**diagnoses**: The document explicitly states "Diagnoses: G47.33, E66.9"
- G47.33 (which is obstructive sleep apnea with mild hypoxemia)
- E66.9 (obesity, unspecified)
These are literal ICD-10-CM codes in the document, so both should be included.
**medications**: The document states "She takes sertraline 100 mg daily."
- Sertraline is the generic name (brand name would be Zoloft)
- It's not discontinued or stopped
- I should list it as lower-case with no dose: "sertraline"
**referring_npi**: The document shows "R. Delacroix, MD" from "Delacroix Family Practice, Garland TX"
- No 10-digit NPI is provided in the document
- The instructions say "If no complete 10-digit NPI is present, return an empty array"
- So this should be an empty array []
**dates_of_service**:
- The referral is for a sleep study to be performed, but no specific date is given
- The document doesn't specify when the sleep study will occur
- It says "Please see my patient for a sleep study" which is a request, not a documented service with a date
- No dates of service are actually provided in the document
- This should be an empty array []
**allergies**: The document states "No allergies."
- This confirms the patient has no known allergies or intolerances to report
- This should be an empty array []
Compiling the final JSON output:
```json
{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
```
Checking against the requirements once more - the diagnosis codes are literal matches, sertraline is properly normalized to generic lowercase form, and the empty arrays are justified since no NPI, service dates, or allergies were documented.
entioned—this is a referral request, not a completed service. Nothing anticipated or unbooked counts, so an empty array is right.
Allergies: document states "No allergies," so that stays empty too.
Everything checks out.
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 650 tokens
Let me parse this document carefully according to the requirements.
The task asks me to extract:
1. diagnoses - ICD-10-CM codes that appear LITERALLY in the document describing member's conditions
2. medications - current medications as lower-case generic names with no dose/route/frequency, converting brand names to generic
3. referring_npi - National Provider Identifier of referring/ordering provider, 10-digit string
4. dates_of_service - dates being requested or performed in YYYY-MM-DD format
5. allergies - allergies and intolerances, lower-case, substance only, no reaction description
Let me go through the document:
**Diagnoses:**
The document states: "Diagnoses: G47.33, E66.9"
- G47.33 - This is an ICD-10-CM code
- E66.9 - This is an ICD-10-CM code
Both appear literally in the document. I should include both.
**Medications:**
"She takes sertraline 100 mg daily."
- Sertraline is the generic name
- I need to list it in lower-case: "sertraline"
- Omit dose, route, frequency per instructions
- This is current, not discontinued
**Referring NPI:**
The document is signed by "R. Delacroix, MD" from "Delacroix Family Practice, Garland TX"
However, I need a 10-digit National Provider Identifier. The document does NOT provide an NPI number. The instructions state: "If no complete 10-digit NPI is present, return an empty array."
So referring_npi should be an empty array.
**Dates of Service:**
The document is a referral letter. It says "Please see my patient for a sleep study" but does not specify a date for the service. The instruction says "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." This appears to be a referral for a future appointment that hasn't been scheduled yet.
The system reminder's date of 2026-09-02 is just contextual metadata, not something extracted from the document itself, so it doesn't count as a date of service. Since no service date is stated in the letter, this field stays empty.
**Allergies:**
"No allergies." — nothing to list here, so this field is empty too.
Final check:
- diagnoses: ["G47.33", "E66.9"]
- medications: ["sertraline"]
- referring_npi: []
- dates_of_service: []
- allergies: []
json{
"diagnoses": [
"G47.33",
"E66.9"
],
"medications": [
"sertraline"
],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 494 tokens
Let me carefully extract the required information from this referral document.
**diagnoses**: The document explicitly lists "Diagnoses: G47.33, E66.9"
- G47.33: This is an ICD-10-CM code for obstructive sleep apnea with moderate obstruction
- E66.9: This is an ICD-10-CM code for unspecified obesity
These are diagnosis codes that appear literally in the document and describe the member's own conditions. I should include both.
**medications**: The document states "She takes sertraline 100 mg daily."
- Sertraline is the generic name
- I need to list it as lower-case generic name with no dose, route, or frequency
- So: "sertraline"
- The document doesn't say it was discontinued, so it's a current medication
**referring_npi**: The document shows "R. Delacroix, MD" and "Delacroix Family Practice, Garland TX" but no 10-digit NPI is provided. The instructions say "If no complete 10-digit NPI is present, return an empty array." So this should be an empty array.
**dates_of_service**: The document doesn't include any specific dates of service. It says "Please see my patient for a sleep study" which is a request for a future service, but no date is given. The document describes the service as not yet booked/anticipated. The date the document was written is not provided either. So this should be an empty array.
**allergies**: The document explicitly states "No allergies." This means there are no allergies to report. So this should be an empty array.
Let me double-check:
- diagnoses: ["G47.33", "E66.9"] - Yes, these appear literally
- medications: ["sertraline"] - Yes, sertraline is current, generic name, no dose/route/frequency
- referring_npi: [] confirmed, no 10-digit number anywhere in the text
- dates_of_service: [] confirmed, only a future/unscheduled request exists
- allergies: [] confirmed, explicitly negated in the note
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100DeepSeek V3.2 · DeepSeek2.7s · 56 out | 1.8s · 56 out | 1.9s · 56 out
json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"G47.33",
"E66.9"
],
"got": [
"G47.33",
"E66.9"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"sertraline"
],
"got": [
"sertraline"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["G47.33", "E66.9"],
"medications": ["sertraline"],
"referring_npi": [],
"dates_of_service": [],
"allergies": []
}
Grader detail
json{
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],
"got": [
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]
},
{
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"got": [
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},
{
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}
],
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}