pith:SKKEXQCK
SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification
Small language models distilled from large ones match teacher performance on structured patient identifiers in clinical notes at 0.88 precision and 0.86 recall on standard hardware.
arxiv:2605.03301 v2 · 2026-05-05 · cs.CL · cs.AI
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Claims
Our best distilled model matches its teacher on structured PHI categories (DATE, DOCTOR, ID, PATIENT, PHONE) and achieves micro-averaged span-level precision of 0.88 and recall of 0.86 on standard workstation hardware.
The set-cover diversity sampling combined with human-in-the-loop adjudication yields a dataset representative of modern clinical narratives that supports generalization beyond the sampled notes and institutions.
SHIELD dataset and distilled DeBERTa v3 model achieve 0.88 micro precision and 0.86 recall on PHI de-identification while matching teacher performance on structured categories.
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| First computed | 2026-07-02T00:18:29.478279Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
92944bc04af07bc6d9afa75c29912f05ceec9288e8456ca80a8bb47e5bfd87c5
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/SKKEXQCK6B54NWNPU5OCTEJPAX \
| jq -c '.canonical_record' \
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Canonical record JSON
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