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. 2020 Sep;11(5):669-677.
doi: 10.1002/jrsm.1431. Epub 2020 Aug 5.

The NICE UK geographic search filters for MEDLINE and Embase (Ovid): Post-development study to further evaluate precision and number-needed-to-read when retrieving UK evidence

Lynda Ayiku  1 , Paul Levay  1 , Thomas Hudson  1 , Amy Finnegan  1
Affiliations

The NICE UK geographic search filters for MEDLINE and Embase (Ovid): Post-development study to further evaluate precision and number-needed-to-read when retrieving UK evidence

Lynda Ayiku et al. Res Synth Methods. 2020 Sep.

Abstract

Background: The National Institute for Health and Care Excellence's (NICE) United Kingdom (UK) geographic search filters for MEDLINE and Embase (OVID) retrieve evidence in literature searches for UK-focused research topics with high recall. Their precision and number-needed-to-read (NNR) was examined previously in case studies using a single review. This paper details a larger post-development study that was conducted to test the NICE UK filters' precision and NNR more extensively.

Methods: The filters' recall of included UK references from 100 reviews was calculated. As reproducible search strategies were not available for every review, the MEDLINE filter's precision and NNR were calculated using strategies from 25 reviews. Strategies from nine reviews were used for the Embase filter.

Results: The MEDLINE filter achieved an average of 96.4% recall for the included UK references from the 100 reviews and the Embase filter achieved an average of 97.4% recall. Compared to not using a filter, the MEDLINE filter achieved an average of 98.9% recall for the 25 reviews. Precision was increased by an average of 7.8 times, reducing the NNR from 357 to 46. The Embase filter achieved an average of 97.1% recall for the nine reviews. Precision was increased by an average of 5.1 times, reducing the NNR from 746 to 146.

Conclusion: There is more evidence to demonstrate that the NICE UK filters retrieve the majority of UK evidence from MEDLINE and Embase while increasing precision and reducing NNR. The filters can save time spent on selecting evidence for UK-focused research topics.

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