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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
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 cl...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936746/ https://www.ncbi.nlm.nih.gov/pubmed/36800933 http://dx.doi.org/10.1186/s12874-023-01849-0 |
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author | Fernandez-Felix, Borja M. López-Alcalde, Jesus Roqué, Marta Muriel, Alfonso Zamora, Javier |
author_facet | Fernandez-Felix, Borja M. López-Alcalde, Jesus Roqué, Marta Muriel, Alfonso Zamora, Javier |
author_sort | Fernandez-Felix, Borja M. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01849-0. |
format | Online Article Text |
id | pubmed-9936746 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99367462023-02-18 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 BMC Med Res Methodol Software 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01849-0. BioMed Central 2023-02-17 /pmc/articles/PMC9936746/ /pubmed/36800933 http://dx.doi.org/10.1186/s12874-023-01849-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Fernandez-Felix, Borja M. López-Alcalde, Jesus Roqué, Marta Muriel, Alfonso Zamora, Javier CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models |
title | CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models |
title_full | CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models |
title_fullStr | CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models |
title_full_unstemmed | CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models |
title_short | CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models |
title_sort | charms and probast at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9936746/ https://www.ncbi.nlm.nih.gov/pubmed/36800933 http://dx.doi.org/10.1186/s12874-023-01849-0 |
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