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 journal(Systematic) Review article peer-review


OBJECTIVE: To assess improvement in the completeness of reporting 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 pre-print and peer-reviewed published versions available, were assessed. The primary outcome was the change in percentage adherence to the TRIPOD reporting guidelines between pre-print and published manuscripts.

RESULTS: 19 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 amongst pre-print versions was 33% (min-max: 10 to 68%). The percentage adherence of TRIPOD components increased from pre-print 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: 0-14pp) across all studies. No association was observed between the change in percentage adherence and pre-print score, journal impact factor, or time between journal submission and acceptance.

CONCLUSIONS: Pre-print reporting quality of COVID-19 prediction modelling 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
Issue number1
Early online date14 Dec 2022
Publication statusPublished - Feb 2023

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