Titles versus titles and abstracts for initial screening of articles for systematic reviews
Open accessAbstract
BACKGROUND: There is no consensus on whether screening titles alone or titles and abstracts together is the preferable strategy for inclusion of articles in a systematic review. METHODS: TWO METHODS OF SCREENING ARTICLES FOR INCLUSION IN A SYSTEMATIC REVIEW WERE COMPARED: titles first versus titles and abstracts simultaneously. Each citation found in MEDLINE or Embase was reviewed by two physician reviewers for prespecified criteria: the citation included (1) primary data; (2) the exposure of interest; and (3) the outcome of interest. RESULTS: There were 2965 unique citations. The titles first strategy resulted in an immediate rejection of 2558 (86%) of the records after reading the title alone, requiring review of 239 titles and abstracts, and subsequently 176 full text articles. The simultaneous titles and abstracts review led to rejection of 2782 citations (94%) and review of 183 full text articles. Interreviewer agreement to include an article for full text review using the titles-first screening strategy was 89%-94% (kappa = 0.54) and 96%-97% (kappa = 0.56) for titles and abstracts combined. The final systematic review included 13 articles, all of which were identified by both screening strategies (yield 100%, burden 114%). Precision was higher in the titles and abstracts method (7.1% versus 3.2%) but recall was the same (100% versus 100%), leading to a higher F-measure for the titles and abstracts approach (0.1327 versus 0.0619). CONCLUSION: Screening via a titles-first approach may be more efficient than screening titles and abstracts together.
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Due to high screening burden, title/abstract screening can consume a large proportion of total human resource time spent on a systematic review.
“While screening of titles and abstracts represents only one step in the series of tasks involved in the conduct of SRs, due to the high screening burden, the resources for this step can be a large proportion of the total human resource time spent on the review [6].”
From An evaluation of DistillerSR’s machine learning-based prioritization tool for title/abstract screening – impact on reviewer-relevant outcomes (2020) · cites this paper as [6]
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References 12 · Cited by 202
- Authors
- Mateen, F., Oh, Tergas, Bhayani, & Kamdar
- Affiliations
- Johns Hopkins University (US)
- Howard University Hospital (US)
- Corresponding author
- Mateen, Farrah
- ORCID
- Mateen, Farrah
- Venue
- Clinical Epidemiology
- Publisher
- Informa UK Limited
- Published
- 2013-03
- Volume
- 5
- Pages
- 89
- ISSN
- 1179-1349 (ISSN-L)
- PMC
- PMC3604876
- OpenAlex
- W2052892565
- Cited by
- 202
- References
- 12
- Citations by year
- 2026: 15 · 2025: 27 · 2024: 25 · 2023: 24 · 2022: 15 · 2021: 23 · 2020: 19 · 2019: 16 · 2018: 16 · 2017: 5 · 2016: 5 · 2015: 4 · 2014: 4 · 2013: 4
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Clinical Epidemiology (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Funders
- Johns Hopkins University
- National Institutes of Health
- Johns Hopkins Bloomberg School of Public Health
- National Heart, Lung, and Blood Institute (F32HL104901)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Health Professions › Health Information Management › Methodologies in Health Research and Practice
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- systematic review screening, title and abstract screening, MEDLINE, Embase
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