Comparison of top-performing search strategies for detecting clinically sound treatment studies and systematic reviews in MEDLINE and EMBASE (original) (raw)
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Journal of the Medical Library Association : JMLA, 2014
Background: Since 2005, International Committee of Medical Journal Editors (ICMJE) member journals have required that clinical trials be registered in publicly available trials registers before they are considered for publication. Objectives: The research explores whether it is adequate, when searching to inform systematic reviews, to search for relevant clinical trials using only public trials registers and to identify the optimal search approaches in trials registers. Methods: A search was conducted in ClinicalTrials.gov and the International Clinical Trials Registry Platform (ICTRP) for research studies that had been included in eight systematic reviews. Four search approaches (highly sensitive, sensitive, precise, and highly precise) were performed using the basic and advanced interfaces in both resources.
Journal of Evaluation in Clinical Practice, 2010
Objective The aim of this study was to compare the performance of three search methods in the retrieval of relevant clinical trials from PubMed to answer specific clinical questions. Methods Included studies of a sample of 100 Cochrane reviews which recorded in PubMed were considered as the reference standard. The search queries were formulated based on the systematic review titles. Precision, recall and number of retrieved records for limiting the results to clinical trial publication type, and using sensitive and specific clinical queries filters were compared. The number of keywords, presence of specific names of intervention and syndrome in the search keywords were used in a model to predict the recalls and precisions.
Optimized search strategy for detecting scientifically strong studies on treatment through PubMed
Internal and Emergency Medicine, 2012
Our study was designed to optimize the search strategies based on the work of Haynes et al. for detecting randomized controlled trials (RCTs) through PubMed. In particular, we aimed to improve precision for broad and narrow searches on interventional studies. We used in addition to the string suggested by the Hedge Team the following: {NOT ((animals [mh] NOT humans [mh]) OR (review [pt] OR meta-analysis [pt]))} and tested its effectiveness. The search was carried out on a year's worth of articles from the PubMed database. We analyzed 35,590 bibliographic citations about four relevant major topics in internal medicine (hypertension, diabetes, heart failure, and hepatitis). Precision, percentage gain between the Hedge Team search strategies and the new one were computed and reported in the text. Moreover, a pooled analysis was carried out in terms of absolute precision difference. We observed better precision for both broad and narrow searches. However, effective gain resulted only for broad searches. In this case, bibliographic citation recall effectively reduced (-24 to -35 % retrieved citation with a gain of 32-54 %) without loss of information. The search strategy improved broad searches regarding each of the four considered topics. We think this new search strategy, based on a previous work of the Hedge team, could be a step forward and can save some time by researchers.
Systematic Reviews, 2016
Background: Previously, we reported on the low recall of Google Scholar (GS) for systematic review (SR) searching. Here, we test our conclusions further in a prospective study by comparing the coverage, recall, and precision of SR search strategies previously performed in Embase, MEDLINE, and GS. Methods: The original search results from Embase and MEDLINE and the first 1000 results of GS for librarianmediated SR searches were recorded. Once the inclusion-exclusion process for the resulting SR was complete, search results from all three databases were screened for the SR's included references. All three databases were then searched post hoc for included references not found in the original search results. Results: We checked 4795 included references from 120 SRs against the original search results. Coverage of GS was high (97.2 %) but marginally lower than Embase and MEDLINE combined (97.5 %). MEDLINE on its own achieved 92.3 % coverage. Total recall of Embase/MEDLINE combined was 81.6 % for all included references, compared to GS at 72.8 % and MEDLINE alone at 72.6 %. However, only 46.4 % of the included references were among the downloadable first 1000 references in GS. When examining data for each SR, the traditional databases' recall was better than GS, even when taking into account included references listed beyond the first 1000 search results. Finally, precision of the first 1000 references of GS is comparable to searches in Embase and MEDLINE combined. Conclusions: Although overall coverage and recall of GS are high for many searches, the database does not achieve full coverage as some researchers found in previous research. Further, being able to view only the first 1000 records in GS severely reduces its recall percentages. If GS would enable the browsing of records beyond the first 1000, its recall would increase but not sufficiently to be used alone in SR searching. Time needed to screen results would also increase considerably. These results support our assertion that neither GS nor one of the other databases investigated, is on its own, an acceptable database to support systematic review searching.
Development of an efficient search filter to retrieve systematic reviews from PubMed
Journal of the Medical Library Association, 2021
Objective: Locating systematic reviews is essential for clinicians and researchers when creating or updating reviews and for decision-making in health care. This study aimed to develop a search filter for retrieving systematic reviews that improves upon the performance of the PubMed systematic review search filter. Methods: Search terms were identified from abstracts of reviews published in Cochrane Database of Systematic Reviews and the titles of articles indexed as systematic reviews in PubMed. Both the precision of the candidate terms and the number of systematic reviews retrieved from PubMed were evaluated after excluding the subset of articles retrieved by the PubMed systematic review filter. Terms that achieved a precision greater than 70% and relevant publication types indexed with MeSH terms were included in the filter search strategy. Results: The search strategy used in our filter added specific terms not included in PubMed's systematic review filter and achieved a 61.3% increase in the number of retrieved articles that are potential systematic reviews. Moreover, it achieved an average precision that is likely greater than 80%. Conclusions: The developed search filter will enable users to identify more systematic reviews from PubMed than the PubMed systematic review filter with high precision.
Optimizing search strategies to identify randomized controlled trials in MEDLINE
BMC Medical Research Methodology, 2006
The Cochrane Highly Sensitive Search Strategy (HSSS), which contains three phases, is widely used to identify Randomized Controlled Trials (RCTs) in MEDLINE. Lefebvre and Clarke suggest that reviewers might consider using four revisions of the HSSS. The objective of this study is to validate these four revisions: combining the free text terms volunteer, crossover, versus, and the Medical Subject Heading CROSS-OVER STUDIES with the top two phases of the HSSS, respectively.