系统性审查生产中的自动引用搜索:一个模拟研究研究
Darren Rajit1, Lan Du2, Helena Teede1,3
1Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, Victoria, Australia.
Research synthesis methods
|February 2, 2026
概括
使用OpenAlex和Semantic Scholar的自动引用搜索显示了系统审查的前景. 虽然它提高了精度和F1分数,但回忆率较低,这表明它可以作为补充策略使用.
科学领域:
- 信息科学 信息科学 信息科学
- 图书统计学 图书统计学
- 系统审查方法论 系统审查方法论
背景情况:
- 像OpenAlex和Semantic Scholar这样的文献汇总器为系统评论中的自动引用搜索提供了潜在的可能性.
- 自动化方法可以提高系统审查生产的效率.
研究的目的:
- 对标准策略进行自动引用搜索的性能进行评估.
- 确定影响自动引用搜索性能的因素.
主要方法:
- 在OpenAlex和Semantic Scholar的27个系统评论中模拟了自动引用搜索.
- 绩效指标包括回忆,精度和F1-F3分数,与原始审查搜索策略相比.
- 分析的因素包括研究领域,文章数量,种子文章特征和API选择.
主要成果:
- 自动引用搜索在精度 (p <0.05) 和F1得分 (p <0.05) 方面超过了标准策略.
- 回忆 (p < 0.05) 和F3评分 (p < 0.05) 没有优于标准策略.
- 根据研究领域的表现不同,环境管理方面的表现高于社会政策.
结论:
- 自动引用搜索最好用作补充策略,当准确性优先于回忆时.
- 它的卓越精度和F1评分表明它在特定的系统审查环境中具有实用性.
- 较低的回忆和F3分数表明当全面回忆至关重要时,存在局限性.
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