系统评价文献检索的最佳数据库组合：一项前瞻性探索性研究。

Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study.

机构信息

Medical Library, Erasmus MC, Erasmus University Medical Centre Rotterdam, 3000 CS, Rotterdam, the Netherlands.

Spencer S. Eccles Health Sciences Library, University of Utah, Salt Lake City, Utah, USA.

出版信息

Syst Rev. 2017 Dec 6;6(1):245. doi: 10.1186/s13643-017-0644-y.

DOI:10.1186/s13643-017-0644-y

PMID:29208034

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5718002/

Abstract

BACKGROUND

Within systematic reviews, when searching for relevant references, it is advisable to use multiple databases. However, searching databases is laborious and time-consuming, as syntax of search strategies are database specific. We aimed to determine the optimal combination of databases needed to conduct efficient searches in systematic reviews and whether the current practice in published reviews is appropriate. While previous studies determined the coverage of databases, we analyzed the actual retrieval from the original searches for systematic reviews.

METHODS

Since May 2013, the first author prospectively recorded results from systematic review searches that he performed at his institution. PubMed was used to identify systematic reviews published using our search strategy results. For each published systematic review, we extracted the references of the included studies. Using the prospectively recorded results and the studies included in the publications, we calculated recall, precision, and number needed to read for single databases and databases in combination. We assessed the frequency at which databases and combinations would achieve varying levels of recall (i.e., 95%). For a sample of 200 recently published systematic reviews, we calculated how many had used enough databases to ensure 95% recall.

RESULTS

A total of 58 published systematic reviews were included, totaling 1746 relevant references identified by our database searches, while 84 included references had been retrieved by other search methods. Sixteen percent of the included references (291 articles) were only found in a single database; Embase produced the most unique references (n = 132). The combination of Embase, MEDLINE, Web of Science Core Collection, and Google Scholar performed best, achieving an overall recall of 98.3 and 100% recall in 72% of systematic reviews. We estimate that 60% of published systematic reviews do not retrieve 95% of all available relevant references as many fail to search important databases. Other specialized databases, such as CINAHL or PsycINFO, add unique references to some reviews where the topic of the review is related to the focus of the database.

CONCLUSIONS

Optimal searches in systematic reviews should search at least Embase, MEDLINE, Web of Science, and Google Scholar as a minimum requirement to guarantee adequate and efficient coverage.

摘要

背景

在系统评价中，搜索相关文献时，建议使用多个数据库。但是，由于搜索策略的语法是针对特定数据库的，因此搜索数据库既繁琐又耗时。我们旨在确定在系统评价中进行高效搜索所需的最佳数据库组合，以及已发表的综述中当前的实践是否合适。虽然之前的研究确定了数据库的覆盖范围，但我们分析了原始搜索中实际检索到的系统评价。

方法

自 2013 年 5 月以来，第一作者前瞻性地记录了他在所在机构进行的系统评价搜索的结果。使用 PubMed 确定使用我们的搜索策略结果发表的系统评价。对于每篇发表的系统评价，我们提取纳入研究的参考文献。使用前瞻性记录的结果和发表的研究，我们计算了单个数据库和数据库组合的召回率、精度和需要阅读的数量。我们评估了数据库和组合实现不同召回率（即 95%）的频率。对于最近发表的 200 篇系统评价的样本，我们计算了使用足够的数据库来确保 95%的召回率的数量。

结果

共纳入 58 篇发表的系统评价，共计 1746 篇由我们的数据库搜索确定的相关参考文献，而 84 篇参考文献是通过其他搜索方法检索到的。16%的纳入参考文献（291 篇文章）仅在单个数据库中找到；Embase 产生的独特参考文献最多（n=132）。Embase、MEDLINE、Web of Science Core Collection 和 Google Scholar 的组合效果最佳，总体召回率为 98.3%，72%的系统评价达到 100%的召回率。我们估计，60%的已发表系统评价没有检索到所有可用相关参考文献的 95%，因为许多评价未能搜索到重要数据库。其他专业数据库，如 CINAHL 或 PsycINFO，在一些与数据库重点相关的评价中添加了独特的参考文献。