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考克兰中央检索服务对识别随机对照试验具有较高的敏感性:一项回顾性分析。

Cochrane Centralised Search Service showed high sensitivity identifying randomized controlled trials: A retrospective analysis.

机构信息

Radcliffe Department of Medicine, Cochrane Dementia and Cognitive Improvement Group, Oxford University, Oxford, UK.

Metaxis Ltd, Oxford, UK.

出版信息

J Clin Epidemiol. 2020 Nov;127:142-150. doi: 10.1016/j.jclinepi.2020.08.008. Epub 2020 Aug 13.

Abstract

BACKGROUND AND OBJECTIVES

The Cochrane Central Register of Controlled Trials (CENTRAL) is compiled from a number of sources, including PubMed and Embase. Since 2017, we have increased the number of sources feeding into CENTRAL and improved the efficiency of our processes through the use of application programming interfaces, machine learning, and crowdsourcing.Our objectives were twofold: (1) Assess the effectiveness of Cochrane's centralized search and screening processes to correctly identify references to published reports which are eligible for inclusion in Cochrane systematic reviews of randomized controlled trials (RCTs). (2) Identify opportunities to improve the performance of Cochrane's centralized search and screening processes to identify references to eligible trials.

METHODS

We identified all references to RCTs (either published journal articles or trial registration records) with a publication or registration date between 1st January 2017 and 31st December 2018 that had been included in a Cochrane intervention review. We then viewed an audit trail for each included reference to determine if it had been identified by our centralized search process and subsequently added to CENTRAL.

RESULTS

We identified 650 references to included studies with a publication year of 2017 or 2018. Of those, 634 (97.5%) had been captured by Cochrane's Centralised Search Service. Sixteen references had been missed by the Cochrane's Centralised Search Service: six had PubMed-not-MEDLINE status, four were missed by the centralized Embase search, three had been misclassified by Cochrane Crowd, one was from a journal not indexed in MEDLINE or Embase, one had only been added to Embase in 2019, and one reference had been rejected by the automated RCT machine learning classifier. Of the sixteen missed references, eight were the main or only publication to the trial in the review in which it had been included.

CONCLUSION

This analysis has shown that Cochrane's centralized search and screening processes are highly sensitive. It has also helped us to understand better why some references to eligible RCTs have been missed. The CSS is playing a critical role in helping to populate CENTRAL and is moving us toward making CENTRAL a comprehensive repository of RCTs.

摘要

背景和目的

考科兰中心对照试验注册库(CENTRAL)是从多个来源编译而成的,包括 PubMed 和 Embase。自 2017 年以来,我们通过使用应用程序编程接口、机器学习和众包,增加了向 CENTRAL 输入的源数量,并提高了我们的流程效率。我们的目标有两个:(1)评估考科兰集中搜索和筛选过程识别符合考科兰随机对照试验(RCT)系统评价纳入标准的已发表报告参考文献的有效性。(2)确定提高考科兰集中搜索和筛选过程识别合格试验参考文献性能的机会。

方法

我们确定了 2017 年 1 月 1 日至 2018 年 12 月 31 日期间发表的包含 RCT 参考文献(发表的期刊文章或试验注册记录)的所有参考文献,并纳入考科兰干预性综述。然后,我们查看了每个纳入参考文献的审核跟踪记录,以确定它是否通过我们的集中搜索过程识别并随后添加到 CENTRAL。

结果

我们确定了 2017 年或 2018 年发表的 650 篇纳入研究的参考文献。其中,634 篇(97.5%)被考科兰集中搜索服务捕获。考科兰集中搜索服务遗漏了 16 篇参考文献:6 篇有 PubMed-非 MEDLINE 状态,4 篇在集中式 Embase 搜索中遗漏,3 篇被 Cochrane Crowd 错误分类,1 篇来自未在 MEDLINE 或 Embase 中索引的期刊,1 篇仅在 2019 年添加到 Embase,1 篇参考文献被自动 RCT 机器学习分类器拒绝。在这 16 篇遗漏的参考文献中,有 8 篇是纳入综述中试验的主要或唯一出版物。

结论

这项分析表明,考科兰的集中搜索和筛选过程具有很高的灵敏度。它还帮助我们更好地了解为什么一些合格 RCT 的参考文献被遗漏了。CSS 正在发挥关键作用,帮助填充 CENTRAL,并使我们朝着使 CENTRAL 成为 RCT 的综合存储库的方向发展。

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