Division of Nephrology, University of Western Ontario, London, Canada.
BMC Med Inform Decis Mak. 2012 Jun 6;12:49. doi: 10.1186/1472-6947-12-49.
Tools to enhance physician searches of Medline and other bibliographic databases have potential to improve the application of new knowledge in patient care. This is particularly true for articles about glomerular disease, which are published across multiple disciplines and are often difficult to track down. Our objective was to develop and test search filters for PubMed, Ovid Medline, and Embase that allow physicians to search within a subset of the database to retrieve articles relevant to glomerular disease.
We used a diagnostic test assessment framework with development and validation phases. We read a total of 22,992 full text articles for relevance and assigned them to the development or validation set to define the reference standard. We then used combinations of search terms to develop 997,298 unique glomerular disease filters. Outcome measures for each filter included sensitivity, specificity, precision, and accuracy. We selected optimal sensitive and specific search filters for each database and applied them to the validation set to test performance.
High performance filters achieved at least 93.8% sensitivity and specificity in the development set. Filters optimized for sensitivity reached at least 96.7% sensitivity and filters optimized for specificity reached at least 98.4% specificity. Performance of these filters was consistent in the validation set and similar among all three databases.
PubMed, Ovid Medline, and Embase can be filtered for articles relevant to glomerular disease in a reliable manner. These filters can now be used to facilitate physician searching.
增强医生在 Medline 和其他书目数据库中搜索的工具具有将新知识应用于患者护理的潜力。对于发表在多个学科且难以追踪的肾小球疾病相关文章，尤其如此。我们的目标是开发和测试 PubMed、Ovid Medline 和 Embase 的搜索过滤器，以便医生可以在数据库的一个子集内进行搜索，以检索与肾小球疾病相关的文章。
我们使用了具有开发和验证阶段的诊断测试评估框架。我们总共阅读了 22992 篇全文文章以确定相关性，并将它们分配到开发或验证集以定义参考标准。然后，我们使用搜索词的组合来开发 997298 个独特的肾小球疾病过滤器。每个过滤器的结果衡量指标包括敏感性、特异性、精度和准确性。我们为每个数据库选择了最佳的敏感和特异的搜索过滤器，并将其应用于验证集以测试性能。
在开发集中，高性能过滤器的敏感性至少达到 93.8%，特异性至少达到 98.4%。针对敏感性优化的过滤器的敏感性至少达到 96.7%，针对特异性优化的过滤器的特异性至少达到 98.4%。这些过滤器在验证集中的性能一致，并且在所有三个数据库中相似。
可以可靠地对 PubMed、Ovid Medline 和 Embase 进行过滤，以获取与肾小球疾病相关的文章。这些过滤器现在可以用于方便医生搜索。
