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CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models.
Fernandez-Felix, Borja M; López-Alcalde, Jesus; Roqué, Marta; Muriel, Alfonso; Zamora, Javier.
Afiliación
  • Fernandez-Felix BM; Clinical Biostatistics Unit, Hospital Universitario Ramón y Cajal. IRYCIS, Madrid, Spain. borjamanuel.fernandez@salud.madrid.org.
  • López-Alcalde J; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain. borjamanuel.fernandez@salud.madrid.org.
  • Roqué M; Clinical Biostatistics Unit, Hospital Universitario Ramón y Cajal. IRYCIS, Madrid, Spain.
  • Muriel A; CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain.
  • Zamora J; Institute for Complementary and Integrative Medicine, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
BMC Med Res Methodol ; 23(1): 44, 2023 02 17.
Article en En | MEDLINE | ID: mdl-36800933
ABSTRACT

BACKGROUND:

Systematic reviews of studies of clinical prediction models are becoming increasingly abundant in the literature. Data extraction and risk of bias assessment are critical steps in any systematic review. CHARMS and PROBAST are the standard tools used for these steps in these reviews of clinical prediction models.

RESULTS:

We developed an Excel template for data extraction and risk of bias assessment of clinical prediction models including both recommended tools. The template makes it easier for reviewers to extract data, to assess the risk of bias and applicability, and to produce results tables and figures ready for publication.

CONCLUSION:

We hope this template will simplify and standardize the process of conducting a systematic review of prediction models, and promote a better and more comprehensive reporting of these systematic reviews.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Pronóstico Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: España

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Pronóstico Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: España