ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2026
Large language models are comparable with commonly used statistical software: A validation of GPT 5.1 for frequentist meta-analysis in orthopaedics.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Agreement between ChatGPT and human-derived multilevel meta-analyses: a reproducibility study across clinical evidence syntheses.BMC medical research methodology · 2026Pooled it
- Large language models are comparable with commonly used statistical software: A validation of GPT 5.1 for frequentist meta-analysis in orthopaedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
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5 authors.
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Abstract
purposeThe purpose of this study was to evaluate whether Chat Generative Pre-trained Transformer (ChatGPT; Version 5.1) can reproduce frequentist meta-analytic calculations with an accuracy comparable to established statistical software in orthopaedic research.
methodsIn this methodological comparison study, data from two previously published orthopaedic meta-analyses with identical statistical architectures as reference standards were used. Between-study variance (τ
resultsAcross seven evaluated outcomes, ChatGPT-5.1 reproduced the direction of effects in all cases. Deviations compared with reference meta-analyses were classified as minor in three outcomes (43%), moderate in one outcome (14%) and major in three outcomes (43%). Agreement was highest in low-heterogeneity settings, whereas substantial deviations occurred in outcomes with pronounced between-study heterogeneity, particularly under random-effects models.
conclusionChatGPT-5.1 demonstrates emerging capability to approximate frequentist meta-analytic calculations, particularly in low-heterogeneity settings. However, its tendency to underestimate between-study variability and to deviate in complex random-effects scenarios limits its reliability as a standalone tool. At present, large language models may support exploratory analyses but cannot fully replace dedicated statistical software for meta-analyses in orthopaedic research. LEVEL OF EVIDENCE: Level III.
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