Development and validation of a geographic search filter for MEDLINE (PubMed) to identify studies about Germany

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Alexander Pachanov, Catharina Münte, Julian Hirt, Dawid Pieper
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Abstract

While geographic search filters exist, few of them are validated and there are currently none that focus on Germany. We aimed to develop and validate a highly sensitive geographic search filter for MEDLINE (PubMed) that identifies studies about Germany. First, using the relative recall method, we created a gold standard set of studies about Germany, dividing it into ‘development’ and ‘testing’ sets. Next, candidate search terms were identified using (i) term frequency analyses in the ‘development set’ and a random set of MEDLINE records; and (ii) a list of German geographic locations, compiled by our team. Then, we iteratively created the filter, evaluating it against the ‘development’ and ‘testing’ sets. To validate the filter, we conducted a number of case studies (CSs) and a simulation study. For this validation we used systematic reviews (SRs) that had included studies about Germany but did not restrict their search strategy geographically. When applying the filter to the original search strategies of the 17 SRs eligible for CSs, the median precision was 2.64% (interquartile range [IQR]: 1.34%–6.88%) versus 0.16% (IQR: 0.10%–0.49%) without the filter. The median number-needed-to-read (NNR) decreased from 625 (IQR: 211–1042) to 38 (IQR: 15–76). The filter achieved 100% sensitivity in 13 CSs, 85.71% in 2 CSs and 87.50% and 80% in the remaining 2 CSs. In a simulation study, the filter demonstrated an overall sensitivity of 97.19% and NNR of 42. The filter reliably identifies studies about Germany, enhancing screening efficiency and can be applied in evidence syntheses focusing on Germany.

Abstract Image

为 MEDLINE(PubMed)开发并验证地理搜索过滤器,以识别有关德国的研究。
虽然存在地理搜索过滤器,但其中很少有经过验证的,目前也没有任何一种过滤器是针对德国的。我们的目标是为 MEDLINE (PubMed)开发并验证一种高灵敏度的地理搜索过滤器,以识别有关德国的研究。首先,我们使用相对召回法创建了一个关于德国的金标准研究集,将其分为 "开发 "集和 "测试 "集。接下来,我们使用以下方法确定了候选搜索词:(i) 对 "发展集 "和随机 MEDLINE 记录集进行词频分析;(ii) 我们团队编制的德国地理位置列表。然后,我们反复创建过滤器,并根据 "开发集 "和 "测试集 "对其进行评估。为了验证该过滤器,我们进行了大量案例研究(CS)和模拟研究。在验证过程中,我们使用了系统综述(SR),这些综述包含了有关德国的研究,但并未对其搜索策略进行地域限制。当对符合 CSs 条件的 17 篇 SR 的原始检索策略应用筛选器时,中位精确度为 2.64%(四分位距[IQR]:1.34%-6.88%),而未应用筛选器时为 0.16%(四分位距[IQR]:0.10%-0.49%)。所需读数(NNR)的中位数从625(IQR:211-1042)降至38(IQR:15-76)。该过滤器在 13 个 CS 中的灵敏度达到 100%,在 2 个 CS 中达到 85.71%,在其余 2 个 CS 中分别达到 87.50% 和 80%。在一项模拟研究中,该过滤器的总体灵敏度为 97.19%,NNR 为 42。该过滤器能可靠地识别有关德国的研究,提高了筛选效率,可用于以德国为重点的证据综述。
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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
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