在生物医学研究中，旋转是对发现的过度解释，它正在成为一个日益关注的问题。到目前为止，在肿瘤学的预后模型研究中尚未评估旋转的存在，包括开发和验证个体化风险预测模型的研究。我们进行了系统性回顾，搜索了MEDLINE和EMBASE数据库中于2019年1月1日至2019年5月9日之间发表的使用机器学习开发和验证预后模型的肿瘤相关研究。我们使用现有的旋转框架，描述高度建议旋转实践的领域。我们包括了62篇出版物（包括152个开发的模型；37个验证模型）。在27%的研究中，报告在方法和结果之间的不一致性，是由于额外分析和选择性报告引起的。32项研究（36项适用研究中的32项）在讨论中报告了开发的模型之间的比较，并主要使用歧视力量度来支持他们的主张（78%）。35项研究（56%）在标题、摘要、结果、讨论或结论中使用了过于强烈或具有引导作用的词语。当阅读、解释和使用肿瘤学中开发和验证预后模型的研究时，需要考虑旋转的可能性。研究人员应该仔细报告他们的预后模型研究，使用反映实际结果和证据强度的词语。版权所有©2023年作者。由Elsevier Inc.出版，保留所有权利。

In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualised risk prediction.We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 01/01/2019 and 05/09/2019. We used existing spin frameworks and described areas of highly suggestive spin practices.We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion or conclusion.The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.