pith:OGJW6B2S
Using reasoning LLMs to extract SDOH events from clinical notes
Reasoning LLMs extract social and environmental health factors from clinical notes at competitive accuracy
arxiv:2604.13502 v2 · 2026-04-15 · cs.CL
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Record completeness
Claims
Our approach achieved a micro-F1 score of 0.866, demonstrating competitive performance compared to the leading models.
That the combination of guideline-integrated prompts, curated few-shot examples, self-consistency, and post-processing will produce reliable structured outputs on unseen clinical notes without extensive per-site tuning or validation.
Reasoning LLMs using concise prompts, few-shot examples, self-consistency, and post-processing extract SDOH events at micro-F1 0.866, matching leading BERT models.
Receipt and verification
| First computed | 2026-05-27T02:05:19.734721Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
71936f075243203ffd604a18b1370bdeb78ec481cbbe294b5ba94da7b8a47ba3
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/OGJW6B2SIMQD77LAJIMLCNYL32 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 71936f075243203ffd604a18b1370bdeb78ec481cbbe294b5ba94da7b8a47ba3
Canonical record JSON
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