pith:7MYBZAZX
Can Language Models Identify Side Effects of Breast Cancer Radiation Treatments?
Grounding large language models in clinician-curated lists improves reliability when listing side effects of breast cancer radiation treatments.
arxiv:2605.08439 v2 · 2026-05-08 · cs.CL
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Claims
Grounding outputs in clinician-curated side effect lists substantially improves reliability and robustness while constraints on the number of side effects generated reduce precision.
The clinician-curated reference derived from informed consent documents at two academic centers accurately and comprehensively captures all relevant toxicities broken down by frequency and temporal onset.
LLMs under-recall rare and long-term radiation side effects in breast cancer, show prompt sensitivity, and improve when outputs are grounded in clinician-curated references.
Receipt and verification
| First computed | 2026-05-20T00:04:34.834480Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
fb301c8337c8eee0ea0f0107ad230e9983a1d51c87d8e9b61390f02550bb2d90
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7MYBZAZXZDXOB2QPAED22IYOTG \
| 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: fb301c8337c8eee0ea0f0107ad230e9983a1d51c87d8e9b61390f02550bb2d90
Canonical record JSON
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