aceImpression-regularized finetuning enables small language models to extract cervical spine fractures from radiology reports
We assessed whether small, open-weight large language models (LLMs) can accurately extract vertebral-level acute cervical spine fracture labels from CT radiology reports while supporting on-premises deployment, and whether impression-regularized finetuning improves accuracy, data efficiency, and temporal stability. In this single-center retrospective study, 8,567 adult cervical spine CT reports (2017-2022) were labeled for acute occipital condyle and C1-C7 fractures with neuroradiologist oversight. Reports from…
A regularized finetuning method raised small open-weight models' accuracy at extracting vertebral-level fracture labels, bringing a 4B model close to a much larger zero-shot model while allowing privacy-preserving local use.
The study is single-center and retrospective, and the remaining errors were mainly tied to ambiguous report language and label-boundary cases, so generalizability beyond this setting is not shown.
Evidence
- Peer-reviewedPLOS Digital Health2026-10-21
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Truvace Impact Record TRV-2026-2210, v1: “Impression-regularized finetuning enables small language models to extract cervical spine fractures from radiology reports.” Truvace, 2026-10-23. /record/TRV-2026-2210 (accessed at citation time). sha256 836456391deed8f5…
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