系统性审查 (半自动化) 的数据提取方法:活系统性审查的更新
Lena Schmidt1,2,3, Ailbhe N Finnerty Mutlu4, Rebecca Elmore2
1NIHR Innovation Observatory, Newcastle University, Newcastle upon Tyne, NE4 5TG, UK.
F1000Research
|April 10, 2025
概括
自动数据提取工具通过减少工作量来帮助系统审查. 虽然大型语言模型提供了新的可能性,但它们可能会降低报告质量和可重复性.
科学领域:
- 计算机科学 计算机科学
- 健康科学 卫生科学 卫生科学
- 信息科学 信息科学 信息科学
背景情况:
- 自动数据提取对于有效的系统审查至关重要.
- 这个活生生的系统性审查侧重于已发表的从临床研究报告中提取数据的方法.
研究的目的:
- 在系统性审查中检查和综合已发表的自动数据提取方法.
- 提供与各种审查类型相关的 (半) 自动数据提取文献的概述.
主要方法:
- 在多个数据库 (PubMed,ACL Anthology,arXiv,OpenAlex,dblp) 中进行系统和持续的搜索.
- 使用开源和商业工具的组合进行全文选和数据提取.
- 包括出版物至2024年8月和OpenAlex内容至2024年9月.
主要成果:
- 审查了117份出版物,其中30%使用完整文本,其余使用标题/摘要.
- 96%开发了随机对照试验的分类器,提取了30多个实体,主要是PICOS.
- 在45%的出版物中报告了数据的可用性,在42%的出版物中报告了代码.
结论:
- 对于干预性审查中的自动数据提取存在广泛的证据基础.
- 大型语言模型提供了新的机会,但可能会降低报告质量和可重复性.
- 关系提取和共享代码/数据集的趋势在更新之间保持一致.
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