{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T22:41:50Z","timestamp":1792622510213,"version":"4.5.0"},"reference-count":43,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["esskajournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J. exp. orthop."],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study evaluated five publicly available large language model\u2010based systems using a multidimensional, expert\u2010based assessment of consensus\u2010based clinical questions on bone stress injuries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Ten standardised questions derived from a 2025 international Delphi consensus on bone stress injuries were submitted to ChatGPT\u20105, Perplexity AI, Claude 4, Google Gemini 2.5 and Microsoft Copilot. Responses were independently rated by clinical experts using a multidimensional evaluation framework covering accuracy, comprehensiveness, understanding, reasoning, clarity, potential harm and trust, each rated on a 5\u2010point Likert scale. Median scores were calculated for each question and model, and comparisons were performed using Kruskal\u2013Wallis tests followed by Dunn's post hoc tests with Bonferroni correction. Interrater agreement was assessed using the weighted Gwet's agreement coefficient. Reproducibility was assessed by repeatedly submitting nine questions representing no, partial, and full expert consensus to each model, with response consistency scored from 1 to 3.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Significant differences between models were observed for comprehensiveness, reasoning, clarity and trust (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), with no differences in accuracy or harm. ChatGPT\u20105 and Claude 4 achieved significantly higher comprehensiveness scores, while ChatGPT\u20105 and Google Gemini 2.5 had significantly higher reasoning scores than Microsoft Copilot. Claude 4 demonstrated significantly lower clarity than all other models. ChatGPT\u20105 achieved significantly higher trust scores and showed the highest mean reproducibility score, followed by Perplexity AI and Gemini 2.5; Microsoft Copilot showed the lowest reproducibility. Interrater agreement ranged from fair to almost perfect, with substantial overall agreement.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Publicly available large language model\u2010based systems demonstrated moderate\u2010to\u2010high performance in addressing expert\u2010derived, consensus\u2010based questions on bone stress injuries, suggesting potential to support the communication of consensus\u2010based knowledge. However, performance varied across evaluation dimensions and reproducibility, indicating that large language models may support, but should not replace, primary consensus documents or expert clinical judgement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>N\/A.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jeo2.70939","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T14:12:58Z","timestamp":1791382378000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large language models show distinct multidimensional performance profiles and variable reproducibility in consensus\u2010based bone stress injury"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8914-6863","authenticated-orcid":false,"given":"Silvia","family":"Gandini","sequence":"first","affiliation":[{"name":"Milan Lab Research Department AC Milan S.p.A Milan Italy"},{"name":"Department of Biomedical Sciences University of Padova Padua Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob","family":"Lindskog","sequence":"additional","affiliation":[{"name":"Unit of Physiotherapy, Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy University of Gothenburg Gothenburg Sweden"},{"name":"Sahlgrenska Sports Medicine Center, Sahlgrenska Academy Gothenburg Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinan","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"\u00d6ttl","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Balgrist University Hospital University of Z\u00fcrich Zurich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1249-228X","authenticated-orcid":false,"given":"Richard","family":"Willy","sequence":"additional","affiliation":[{"name":"School of Health &amp; Rehabilitation Sciences The Ohio State University Columbus Ohio USA"},{"name":"Sports Medicine Research Institute, The Ohio State University Wexner Medical Center The Ohio State University Columbus Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo Emilio","family":"Adami","sequence":"additional","affiliation":[{"name":"Health and Science Department World Athletics  Monaco"},{"name":"Physiology and Pharmacology Department \u201cV. 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