Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review

Mohammed T. Hudda*, Lucinda Archer, Maarten van Smeden, Karel G.M. Moons, Gary S. Collins, Ewout W. Steyerberg, Charlotte Wahlich, Johannes B. Reitsma, Richard D. Riley, Ben Van Calster, Laure Wynants

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Objectives: To assess improvement in the completeness of reporting coronavirus (COVID-19) prediction models after the peer review process.

Study Design and Setting: Studies included in a living systematic review of COVID-19 prediction models, with both preprint and peer-reviewed published versions available, were assessed. The primary outcome was the change in percentage adherence to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) reporting guidelines between pre-print and published manuscripts.

Results: Nineteen studies were identified including seven (37%) model development studies, two external validations of existing models (11%), and 10 (53%) papers reporting on both development and external validation of the same model. Median percentage adherence among preprint versions was 33% (min-max: 10 to 68%). The percentage adherence of TRIPOD components increased from preprint to publication in 11/19 studies (58%), with adherence unchanged in the remaining eight studies. The median change in adherence was just 3 percentage points (pp, min-max: 0-14 pp) across all studies. No association was observed between the change in percentage adherence and preprint score, journal impact factor, or time between journal submission and acceptance.

Conclusions: The preprint reporting quality of COVID-19 prediction modeling studies is poor and did not improve much after peer review, suggesting peer review had a trivial effect on the completeness of reporting during the pandemic.
Original languageEnglish
Pages (from-to)75-84
Number of pages10
JournalJournal of Clinical Epidemiology
Early online date14 Dec 2022
Publication statusPublished - Feb 2023


  • Peer review
  • Reporting guidelines
  • Prediction modeling
  • COVID-19
  • Adherence
  • Prognosis and diagnosis


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