Clinical Biostatistics Unit, Instituto Ramón Y Cajal de Investigación Sanitaria, 28034, Madrid, Spain.
CIBER Epidemiología Y Salud Pública (CIBERESP), 28029, Madrid, Spain.
BMC Med Res Methodol. 2022 Apr 10;22(1):107. doi: 10.1186/s12874-022-01595-9.
Systematic reviews (SRs) are valuable resources as they address specific clinical questions by summarizing all existing relevant studies. However, finding all information to include in systematic reviews can be challenging. Methodological search filters have been developed to find articles related to specific clinical questions. To our knowledge, no filter exists for finding studies on the role of prognostic factor (PF). We aimed to develop and evaluate a search filter to identify PF studies in Ovid MEDLINE that has maximum sensitivity.
We followed current recommendations for the development of a search filter by first identifying a reference set of PF studies included in relevant systematic reviews on the topic, and by selecting search terms using a word frequency analysis complemented with an expert panel discussion. We evaluated filter performance using the relative recall methodology.
We constructed a reference set of 73 studies included in six systematic reviews from a larger sample. After completing a word frequency analysis using the reference set studies, we compiled a list of 80 of the frequent methodological terms. This list of terms was evaluated by the Delphi panel for inclusion in the filter, resulting in a final set of 8 appropriate terms. The consecutive connection of these terms with the Boolean operator OR produced the filter. We then evaluated the filter using the relative recall method against the reference set, comparing the references included in the SRs with our new search using the filter. The overall sensitivity of the filter was calculated to be 95%, while the overall specificity was 41%. The precision of the filter varied considerably, ranging from 0.36 to 17%. The NNR (number needed to read) value varied largely from 6 to 278. The time saved by using the filter ranged from 13-70%.
We developed a search filter for OVID-Medline with acceptable performance that could be used in systematic reviews of PF studies. Using this filter could save as much as 40% of the title and abstract screening task. The specificity of the filter could be improved by defining additional terms to be included, although it is important to evaluate any modification to guarantee the filter is still highly sensitive.
系统评价（SR）是有价值的资源，因为它们通过总结所有现有相关研究来解决特定的临床问题。然而，找到所有包含在系统评价中的信息可能具有挑战性。已经开发了方法学搜索过滤器来查找与特定临床问题相关的文章。据我们所知，尚无用于查找预后因素（PF）研究的过滤器。我们旨在开发并评估一种搜索过滤器，以在 Ovid MEDLINE 中找到具有最大灵敏度的 PF 研究。
我们首先通过识别纳入相关主题系统评价的 PF 研究参考集，然后使用词频分析结合专家小组讨论选择搜索词，按照当前的开发搜索过滤器的建议进行操作。我们使用相对召回率方法评估过滤器性能。
我们从更大的样本中构建了一个参考集，其中包含 6 项系统评价中纳入的 73 项研究。在使用参考集研究完成词频分析后，我们列出了 80 个常见的方法术语。德尔菲小组对该术语列表进行了评估，以确定是否将其包含在过滤器中，最终确定了 8 个合适的术语。将这些术语与布尔运算符 OR 连续连接起来，就产生了过滤器。然后，我们使用相对召回率方法对过滤器进行评估，将纳入系统评价的参考与使用过滤器的新搜索进行比较。过滤器的总灵敏度计算为 95%，而总特异性为 41%。过滤器的精度差异很大，范围从 0.36 到 17%。NNR（需阅读的数量）值变化很大，从 6 到 278。使用过滤器可节省 13%至 70%的标题和摘要筛选任务时间。
我们开发了一种针对 OVID-Medline 的搜索过滤器，具有可接受的性能，可用于 PF 研究的系统评价。使用此过滤器可以节省多达 40%的标题和摘要筛选任务时间。可以通过定义要包含的其他术语来提高过滤器的特异性，但是评估任何修改以确保过滤器仍然具有高度敏感性非常重要。
