School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.
Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada.
J Med Internet Res. 2024 May 2;26:e52508. doi: 10.2196/52508.
The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors' application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.
越来越多的机器学习 (ML) 模型论文被提交并发表在《医学互联网研究杂志》和其他 JMIR 出版期刊上。参与此类稿件评审过程的编辑和同行评审员通常要经过多个评审周期，以提高报告的质量和完整性。使用报告准则或清单可以帮助确保提交（和发表）的科学手稿的质量一致性，并避免信息缺失等情况。在这篇社论中，JMIR 出版期刊的编辑讨论了 JMIR 出版期刊关于作者应用报告准则的一般政策，并特别关注在 JMIR 出版期刊中报告 ML 研究的情况，使用了机器学习研究的综合报告准则 (CREMLS)，并举例说明了作者和其他期刊如何使用 CREMLS 清单来确保报告的透明度和严谨性。
