OBJECTIVE: Systematic reviews are important in health care but are expensive to produce and maintain. The authors explore the use of automated transformations of Boolean queries to improve the identification of relevant studies for updates to systematic reviews. MATERIALS AND METHODS: A set of query transformations, including operator substitution, query expansion, and query reduction, were used to iteratively modify the Boolean query used for the original systematic review. The most effective transformation at each stage is identified using information about the studies included and excluded from the original review. A dataset consisting of 22 systematic reviews was used for evaluation. Updated queries were evaluated using the included and excluded studies from the updated version of the review. Recall and precision were used as evaluation measures. RESULTS: The updated queries were more effective than the ones used for the original review, in terms of both precision and recall. The overall number of documents retrieved was reduced by more than half, while the number of relevant documents found increased by 10.3%. CONCLUSIONS: Identification of relevant studies for updates to systematic reviews can be carried out more effectively by using information about the included and excluded studies from the original review to produce improved Boolean queries. These updated queries reduce the overall number of documents retrieved while also increasing the number of relevant documents identified, thereby representing a considerable reduction in effort required by systematic reviewers.

Systematic reviews identify, summarise and synthesise evidence relevant to specific research questions. They are widely used in the field of medicine where they inform health care choices of both professionals and patients. It is important for systematic reviews to stay up to date as evidence changes but this is challenging in a field such as medicine where a large number of publications appear on a daily basis. Developing methods to support the updating of reviews is important to reduce the workload required and thereby ensure that reviews remain up to date. This paper describes a dataset of systematic review updates in the field of medicine created using 25 Cochrane reviews. Each review includes the Boolean query and relevance judgements for both the original and updated versions. The dataset can be used to evaluate approaches to study identification for review updates.