Search Filters for Finding Prognostic and Diagnostic Prediction Studies in Medline to Enhance Systematic Reviews
Open accessAbstract
BACKGROUND: The interest in prognostic reviews is increasing, but to properly review existing evidence an accurate search filer for finding prediction research is needed. The aim of this paper was to validate and update two previously introduced search filters for finding prediction research in Medline: the Ingui filter and the Haynes Broad filter. METHODOLOGY/PRINCIPAL FINDINGS: Based on a hand search of 6 general journals in 2008 we constructed two sets of papers. Set 1 consisted of prediction research papers (n = 71), and set 2 consisted of the remaining papers (n = 1133). Both search filters were validated in two ways, using diagnostic accuracy measures as performance measures. First, we compared studies in set 1 (reference) with studies retrieved by the search strategies as applied in Medline. Second, we compared studies from 4 published systematic reviews (reference) with studies retrieved by the search filter as applied in Medline. Next--using word frequency methods--we constructed an additional search string for finding prediction research. Both search filters were good in identifying clinical prediction models: sensitivity ranged from 0.94 to 1.0 using our hand search as reference, and 0.78 to 0.89 using the systematic reviews as reference. This latter performance measure even increased to around 0.95 (range 0.90 to 0.97) when either search filter was combined with the additional string that we developed. Retrieval rate of explorative prediction research was poor, both using our hand search or our systematic review as reference, and even combined with our additional search string: sensitivity ranged from 0.44 to 0.85. CONCLUSIONS/SIGNIFICANCE: Explorative prediction research is difficult to find in Medline, using any of the currently available search filters. Yet, application of either the Ingui filter or the Haynes broad filter results in a very low number missed clinical prediction model studies.
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Previous guidance for systematic reviews of prediction model studies mainly addressed review aim, searching, critical appraisal, and data extraction, with little guidance on quantitative synthesis of external validation results.
“Previous guidance for systematic reviews of prediction model studies mainly addressed formulation of the review aim, searching,12 critical appraisal (CHARMS)13 and data extraction of relevant studies. There is, however, little guidance on how to quantitatively synthesize the results of external validation studies.”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [12]
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A scoping review of causal methods enabling predictions under hypothetical interventions
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“, MeSH) and free text search terms relating to early pregnancy, prognostic factors, and GDM, along with search filters recommended for prognostic modelling.(38) The search strategy was iteratively developed by Jessica A Grieger and Nahal Habibi in consult with an academic librarian.”
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Self-citationAnticholinergic burden measures and older people’s falls risk: a systematic prognostic review
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Self-citationClinical prediction rules for prognosis and treatment prescription in neck pain: A systematic review
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Self-citationExternally validated prediction models for pre‐eclampsia: systematic review and meta‐analysis
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Protease activity as a prognostic factor for wound healing in venous leg ulcers
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Clinical prediction rules: a systematic review of healthcare provider opinions and preferences
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Bronchopulmonary dysplasia prediction models: a systematic review and meta-analysis with validation
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- Authors
- Geersing, G.-J., Bouwmeester, W., Zuithoff, P., Spijker, R., Leeflang, M., & Moons, K.
- Affiliations
- University Medical Center Utrecht (NL)
- Amsterdam UMC Location University of Amsterdam (NL)
- Corresponding author
- Geersing, Geert-Jan
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-02-29
- Volume
- 7
- Issue
- 2
- Pages
- e32844
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3290602
- OpenAlex
- W2019982728
- Cited by
- 350
- References
- 45
- Citations by year
- 2026: 20 · 2025: 26 · 2024: 39 · 2023: 39 · 2022: 47 · 2021: 47 · 2020: 32 · 2019: 23 · 2018: 24 · 2017: 16 · 2016: 14 · 2015: 11 · 2014: 10 · 2013: 2
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Update notices
- correction (2012-07-10)
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Health Professions › General Health Professions › Health Sciences Research and Education
- Keywords
- search filters, MEDLINE, prognostic research, diagnostic research, clinical prediction model, systematic reviews, search strategy validation
- MeSH terms
- Algorithms, Computers, Humans, Prognosis, ROC Curve, Software, Reproducibility of Results, Multivariate Analysis, Databases, Factual, MEDLINE, Computational Biology, Biomedical Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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