对预后研究和干预研究的系统审查的半自动记录选方法的评估
Isa Spiero1, Artuur M Leeuwenberg1, Karel G M Moons1
1Julius Center for Health Sciences and Primary Care, https://ror.org/0575yy874University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Research synthesis methods
|February 2, 2026
概括
人工智能选工具有效地减少了系统审查 (SRs) 中的工作量. 他们的表现对于预后研究来说仍然很强大,尽管有抽象的异质性,但有助于证据合成.
科学领域:
- 健康科学 卫生科学 卫生科学
- 医疗信息学 医疗信息学
- 证据综合 证据综合
背景情况:
- 系统审查 (SRs) 对于证据综合至关重要,但需要大量的劳动力.
- 人工智能 (AI) 工具通过优先考虑相关记录来加快SR的选阶段.
- 现有的人工智能工具评估侧重于具有结构化摘要的干预研究.
研究的目的:
- 评估AI选工具对预后研究的SRs的有效性,考虑到抽象异质性.
- 评估审查范围,记录数和建模方法对工具性能的影响.
主要方法:
- 使用ASReview工具对预后和干预SRs的回顾性模拟.
- 基于回忆,95%回忆精度和工作量减少 (WSS@95%) 的绩效评估.
- 评估包括审查范围,相关记录数量和建模方法在内的因素.
主要成果:
- 工作负载减少 (WSS@95%) 在预后方面略低 (0.324-0.597) 比干预审查 (0.613-0.895).
- 与干预审查 (0.024-0.057) 相比,预后的准确性更高 (0.115-0.400),归因于更相关的记录.
- 审查范围和建模方法没有显著影响工具性能.
结论:
- 人工智能选工具对于预后研究的SR有效,尽管有更大的抽象异质性.
- 由于预后研究摘要的变化,其有效性并没有受到实质性的阻碍.
- 对于未来关于人工智能工具实施的决策,需要进行进一步的标准化评估.
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