Evidence map›Paper›PMID 33067630›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Refining Boolean queries to identify relevant studies for systematic review updates.

Amal Alharbi, Mark Stevenson

Erratum issuedAbstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Guideline
  4. Review
  5. Review
  6. Article
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Amal AlharbiComputer Science Department, University of Sheffield, Sheffield, United Kingdom.
Mark StevensonComputer Science Department, University of Sheffield, Sheffield, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSystematic 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

methodsA 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.

resultsThe 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%.

conclusionsIdentification 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.

Indexed as

Systematic Reviews as TopicDatasets as TopicInformation Storage and Retrievallexical statisticsquery reformulationscreeningsystematic reviewssystematic reviews updates

Identifiers

PMID33067630
PMCPMC7750994

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.