大规模的流行病学方法审查:使用大型语言模型评估查尔森并发症版本.
Joshua T Fuchs1, Cara Johnson1, Nathan Foster1
1University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
medRxiv : the preprint server for health sciences
|October 3, 2025
概括
一个大型语言模型在超过31,000项研究中确定了查尔森并发症指数 (CCI) 版本. 结果显示,大多数研究都错误地引用了原来的CCI,这阻碍了现代研究中准确的并发症评估.
科学领域:
- 流行病学 流行病学
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 查尔森并发症指数 (CCI) 是流行病学研究中的标准指标,用于评估疾病负担.
- 自1987年以来,最初的CCI已经出现了许多调整,导致当前使用的模两可.
- 在研究中使用的CCI的特定版本及其时间趋势没有得到充分的记录.
研究的目的:
- 开发和验证基于大型语言模型 (LLM) 的方法,自动从科学文献中提取查尔森共同发病率指数 (CCI) 版本信息.
- 分析自2012年以来发表的研究中不同CCI版本的使用趋势.
- 为了解决CCI实施中的模两可,因为经常引用过时版本.
主要方法:
- 一个大型语言模型被训练并应用于从2012年起发表的31,767篇研究文章的数据集.
- 该LLM旨在检测和提取文章文本中的特定查尔森共同发病率指数 (CCI) 版本引用.
- 提取的数据被分析,以确定CCI版本使用的频率和趋势.
主要成果:
- 对31,767篇文章的分析显示,63%的引用单一CCI方法的研究仅引用了1987年的原始出版物.
- 这种依赖原始的CCI对于利用现代数据集的当代研究是有问题的.
- 该研究证明了使用LLM用于方法论工具的自动化文献审查的可行性和可扩展性.
结论:
- 广泛引用原始查尔森并发症指数 (CCI) 出版物导致其在当前研究中的实际应用存在显著的模糊性.
- 基于LLM的方法提供了一个可扩展和高效的解决方案,可以随着时间的推移审查研究方法的实施.
- 这种方法可以通过澄清使用的特定工具来提高流行病学研究的准确性和一致性.
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