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A taxonomy has been developed for outcomes in medical research to help improve knowledge discovery
Open accessAbstract
OBJECTIVES: There is increasing recognition that insufficient attention has been paid to the choice of outcomes measured in clinical trials. The lack of a standardized outcome classification system results in inconsistencies due to ambiguity and variation in how outcomes are described across different studies. Being able to classify by outcome would increase efficiency in searching sources such as clinical trial registries, patient registries, the Cochrane Database of Systematic Reviews, and the Core Outcome Measures in Effectiveness Trials (COMET) database of core outcome sets (COS), thus aiding knowledge discovery. STUDY DESIGN AND SETTING: A literature review was carried out to determine existing outcome classification systems, none of which were sufficiently comprehensive or granular for classification of all potential outcomes from clinical trials. A new taxonomy for outcome classification was developed, and as proof of principle, outcomes extracted from all published COS in the COMET database, selected Cochrane reviews, and clinical trial registry entries were classified using this new system. RESULTS: Application of this new taxonomy to COS in the COMET database revealed that 274/299 (92%) COS include at least one physiological outcome, whereas only 177 (59%) include at least one measure of impact (global quality of life or some measure of functioning) and only 105 (35%) made reference to adverse events. CONCLUSIONS: This outcome taxonomy will be used to annotate outcomes included in COS within the COMET database and is currently being piloted for use in Cochrane Reviews within the Cochrane Linked Data Project. Wider implementation of this standard taxonomy in trial and systematic review databases and registries will further promote efficient searching, reporting, and classification of trial outcomes.
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“The Core Outcome Measures in Effectiveness Trials (COMET) initiative supports standardisation, providing a taxonomy 19 to classify outcomes used in trials.”
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“A strength of this review is the meticulous process of categorising and coding all measures with reference to literature, including the novel application of the COMET taxonomy to Acceptance and Commitment Therapy research. 19”
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Metadata
- Authors
- Dodd, S., Clarke, M., Becker, L., Mavergames, C., Fish, R., & Williamson, P. R.
- Affiliations
- University of Liverpool (GB)
- Queen's University Belfast (GB)
- SUNY Upstate Medical University (US)
- Cochrane (GB)
- University of Manchester (GB)
- Corresponding author
- Williamson, Paula R.
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 96
- Pages
- 84-92
- ISSN
- 0895-4356 (ISSN-L)
- PMC
- PMC5854263
- OpenAlex
- W2778471389
- Cited by
- 625
- References
- 23
- Citations by year
- 2026: 63 · 2025: 97 · 2024: 124 · 2023: 74 · 2022: 84 · 2021: 82 · 2020: 51 · 2019: 34 · 2018: 14
- FWCI
- 69.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Clinical Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, cc-by)
- Research Portal (Queen's University Belfast) (publishedVersion, cc-by)
- Funders
- National Institute for Health and Care Research (NF-SI-0513-10025)
- Medical Research Council (MR/K025635/1)
- Topics
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- clinical trial results, core outcome set, outcome standardization, knowledge discovery, clinical trial registries, adverse events, quality of life outcomes
- MeSH terms
- Clinical Trials as Topic, Humans, Health Knowledge, Attitudes, Practice, Quality of Life, Registries, Research Design, Databases, Factual, Outcome Assessment, Health Care, Data Curation
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