Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review.
J Clin Epidemiol
; 154: 75-84, 2023 02.
Article
in English
| MEDLINE | ID: covidwho-2241601
ABSTRACT
OBJECTIVES:
To assess improvement in the completeness of reporting coronavirus (COVID-19) prediction models after the peer review process. STUDY DESIGN ANDSETTING:
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Observational study
/
Prognostic study
/
Reviews
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
J Clin Epidemiol
Journal subject:
Epidemiology
Year:
2023
Document Type:
Article
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