大型语言模型可以与常用的统计软件进行比较:对GPT 5.1的验证,用于骨科的频率分析
Mikhail Salzmann1,2, Nikolai Ramadanov1,2, Robert Prill1,2
1Centre of Orthopaedics, Traumatology and Plastic Surgery, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany.
概括
在骨科研究中,ChatGPT-5.1显示了对元分析计算的潜力,特别是在低异质性的情况下. 然而,由于低估了可变性和复杂模型中的偏差,它目前无法完全取代统计软件.
科学领域:
- 整形外科的研究.
- 生物统计学 生物统计学
- 医学中的人工智能.
背景情况:
- 频率的元分析对于合成骨科研究中的证据至关重要.
- 已建立的统计软件提供可靠的元分析计算.
- 像ChatGPT这样的大型语言模型的出现需要评估它们在复杂的统计任务中的能力.
研究的目的:
- 评估ChatGPT-5.1在重现频率分析分析计算方面的准确性.
- 为了比较ChatGPT-5.1的性能与已建立的统计软件 (R包元和元喻).
- 确定ChatGPT-5.1在骨科研究中的元分析的可靠性.
主要方法:
- 使用两个骨科元分析数据进行的方法比较.
- 使用Sidik-Jonkman方法估计研究间差异 (τ2).
- 应用Hartung-Knapp调整用于随机效应模型和 τ2=0用于共同效应模型.
- 直接比较ChatGPT-5.1产生的结果与经过验证的参考结果.
主要成果:
- 聊天GPT-5.1重现了所有七个评估结果的影响方向.
- 在异质性较低的环境中,一致性更高.
- 在高异质性设置中观察到实质性偏差,特别是在随机效应模型中,在43%的结果中存在重大差异.
结论:
- 聊天GPT-5.1显示出一个新兴的能力,以近似的元分析计算,特别是在更简单的,低异质性的场景.
- 该模型倾向于低估研究间的变性,并且其在复杂的随机效应模型中的表现限制了其当前的可靠性.
- 大型语言模型可以作为探索性分析的补充工具,但尚未取代用于骨科的元分析的专用统计软件.
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