aceLarge language models show distinct multidimensional performance profiles and variable reproducibility in consensus-based bone stress injury
Purpose This study evaluated five publicly available large language model-based systems using a multidimensional, expert-based assessment of consensus-based clinical questions on bone stress injuries. Methods Ten standardised questions derived from a 2025 international Delphi consensus on bone stress injuries were submitted to ChatGPT-5, Perplexity AI, Claude 4, Google Gemini 2.5 and Microsoft Copilot. Responses were independently rated by clinical experts using a multidimensional evaluation framework covering a…
Publicly available LLM systems showed moderate-to-high performance on expert-derived bone stress injury questions, suggesting they could help communicate consensus-based clinical knowledge.
Performance varied across evaluation dimensions and in reproducibility, so these models should not replace primary consensus documents or expert clinical judgement.
The abstract's results section is truncated, and the authors state that performance and reproducibility varied, so the models cannot replace primary consensus documents or expert clinical judgement.
Evidence
- Peer-reviewedJournal of Experimental Orthopaedics2026-10-07
How should this claim be treated?
Truvace Impact Record TRV-2026-1749, v1: “Large language models show distinct multidimensional performance profiles and variable reproducibility in consensus-based bone stress injury.” Truvace, 2026-10-17. /record/TRV-2026-1749 (accessed at citation time). sha256 68413e2795ebeef8…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1749 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.