Transparent Reporting of Multivariable Prediction Models in Journal and Conference Abstracts: TRIPOD for Abstracts
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2020-07-07
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Abstract
Clear and informative reporting in titles and abstracts is essential to help readers and reviewers identify potentially relevant studies and decide whether to read the full text. Although the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement provides general recommendations for reporting titles and abstracts, more detailed guidance seems to be desirable. The authors present TRIPOD for Abstracts, a checklist and corresponding guidance for reporting prediction model studies in abstracts. A list of 32 potentially relevant items was the starting point for a modified Delphi procedure involving 110 experts, of whom 71 (65%) participated in the web-based survey. After 2 Delphi rounds, the experts agreed on 21 items as being essential to report in abstracts of prediction model studies. This number was reduced by merging some of the items. In a third round, participants provided feedback on a draft version of TRIPOD for Abstracts. The final checklist contains 12 items and applies to journal and conference abstracts that describe the development or external validation of a diagnostic or prognostic prediction model, or the added value of predictors to an existing model, regardless of the clinical domain or statistical approach used.
Keywords
Addicts, Algorithms, Chronic obstructive pulmonary disease, Cohort studies, Endocarditis, Epidemiological statistics, Information storage and retrieval, Machine learning, Research reporting guidelines, Statistical models, Taverne, Journal Article
Citation
Heus, P, Reitsma, J B, Collins, G S, Damen, J A A G, Scholten, R J P M, Altman, D G, Moons, K G M & Hooft, L 2020, 'Transparent Reporting of Multivariable Prediction Models in Journal and Conference Abstracts : TRIPOD for Abstracts', Annals of Internal Medicine, vol. 173, no. 1, pp. 42-47. https://doi.org/10.7326/M20-0193