The NICE search filters for treating and managing COVID-19: validation in MEDLINE and Embase (Ovid).

IF 2.9 4区 医学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Journal of the Medical Library Association Pub Date : 2024-07-01 Epub Date: 2024-07-29 DOI:10.5195/jmla.2024.1806
Paul Levay, Amy Finnegan
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引用次数: 0

Abstract

Objective: In this paper we report how the United Kingdom's National Institute for Health and Care Excellence (NICE) search filters for treating and managing COVID-19 were validated for use in MEDLINE (Ovid) and Embase (Ovid). The objective was to achieve at least 98.9% for recall and 64% for precision.

Methods: We did two tests of recall to finalize the draft search filters. We updated the data from an earlier peer-reviewed publication for the first recall test. For the second test, we collated a set of systematic reviews from Epistemonikos COVID-19 L.OVE and extracted their primary studies. We calculated precision by screening all the results retrieved by the draft search filters from a targeted sample covering 2020-23. We developed a gold-standard set to validate the search filter by using all articles available from the "Treatment and Management" subject filter in the Cochrane COVID-19 Study Register.

Results: In the first recall test, both filters had 99.5% recall. In the second test, recall was 99.7% and 99.8% in MEDLINE and Embase respectively. Precision was 91.1% in a deduplicated sample of records. In validation, we found the MEDLINE filter had recall of 99.86% of the 14,625 records in the gold-standard set. The Embase filter had 99.88% recall of 19,371 records.

Conclusion: We have validated search filters to identify records on treating and managing COVID-19. The filters may require subsequent updates, if new SARS-CoV-2 variants of concern or interest are discussed in future literature.

治疗和管理 COVID-19 的 NICE 搜索过滤器:在 MEDLINE 和 Embase(Ovid)中的验证。
目的:本文报告了英国国家健康与护理优化研究所(NICE)治疗和管理 COVID-19 的搜索过滤器如何在 MEDLINE (Ovid) 和 Embase (Ovid) 中进行验证。目标是召回率至少达到 98.9%,精确率至少达到 64%:我们对召回率进行了两次测试,最终确定了检索筛选器草案。在第一次召回率测试中,我们更新了早期同行评议出版物中的数据。在第二次测试中,我们整理了 Epistemonikos COVID-19 L.OVE 中的一组系统综述,并提取了其中的主要研究。我们从 2020-23 年的目标样本中筛选了草案搜索过滤器检索到的所有结果,从而计算出精确度。我们使用 Cochrane COVID-19 研究注册表中 "治疗与管理 "主题过滤器中的所有文章,建立了一个黄金标准集来验证检索过滤器:在第一次召回测试中,两个筛选器的召回率均为 99.5%。在第二次测试中,MEDLINE 和 Embase 的召回率分别为 99.7% 和 99.8%。在重复记录样本中,精确度为 91.1%。在验证中,我们发现 MEDLINE 过滤器在黄金标准集的 14625 条记录中的召回率为 99.86%。Embase 过滤器在 19,371 条记录中的召回率为 99.88%:结论:我们已经验证了用于识别 COVID-19 治疗和管理记录的检索筛选器。如果未来的文献中出现新的值得关注或感兴趣的 SARS-CoV-2 变体,可能需要对筛选器进行后续更新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of the Medical Library Association
Journal of the Medical Library Association INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
4.10
自引率
10.00%
发文量
39
审稿时长
26 weeks
期刊介绍: The Journal of the Medical Library Association (JMLA) is an international, peer-reviewed journal published quarterly that aims to advance the practice and research knowledgebase of health sciences librarianship. The most current impact factor for the JMLA (from the 2007 edition of Journal Citation Reports) is 1.392.
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