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Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review.
Hudda, Mohammed T; Archer, Lucinda; van Smeden, Maarten; Moons, Karel G M; Collins, Gary S; Steyerberg, Ewout W; Wahlich, Charlotte; Reitsma, Johannes B; Riley, Richard D; Van Calster, Ben; Wynants, Laure.
  • Hudda MT; Population Health Research Institute, St George's University of London, Cranmer Terrace, London, UK SW17 0RE. Electronic address: mhudda@sgul.ac.uk.
  • Archer L; Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK; Institute of Applied Health Research, University of Birmingham, Edgbaston, UK.
  • van Smeden M; Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Moons KGM; Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Collins GS; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK.
  • Steyerberg EW; Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands.
  • Wahlich C; Population Health Research Institute, St George's University of London, Cranmer Terrace, London, UK SW17 0RE.
  • Reitsma JB; Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Riley RD; Institute of Applied Health Research, University of Birmingham, Edgbaston, UK.
  • Van Calster B; Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands; Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
  • Wynants L; Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands; Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, The Netherlands.
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 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.
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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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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