Etiological research aims to uncover causal effects, whilst prediction research aims to forecast an outcome with the best accuracy. Causal and prediction research usually require different methods, and yet their findings may get conflated when reported and interpreted. The aim of the current study is to quantify the frequency of conflation between etiological and prediction research, to discuss common underlying mistakes and provide recommendations on how to avoid these. Observational cohort studies published in January 2018 in the top-ranked journals of six distinct medical fields (Cardiology, Clinical Epidemiology, Clinical Neurology, General and Internal Medicine, Nephrology and Surgery) were included for the current scoping review. Data on conflation was extracted through signaling questions. In total, 180 studies were included. Overall, 26% (n = 46) contained conflation between etiology and prediction. The frequency of conflation varied across medical field and journal impact factor. From the causal studies 22% was conflated, mainly due to the selection of covariates based on their ability to predict without taking the causal structure into account. Within prediction studies 38% was conflated, the most frequent reason was a causal interpretation of covariates included in a prediction model. Conflation of etiology and prediction is a common methodological error in observational medical research and more frequent in prediction studies. As this may lead to biased estimations and erroneous conclusions, researchers must be careful when designing, interpreting and disseminating their research to ensure this conflation is avoided.

病因学研究旨在揭示因果关系，而预测研究旨在以最准确的方式预测结果。因果研究和预测研究通常需要不同的方法，但它们的发现在报告和解释时可能会混淆。本研究的目的是量化病因研究和预测研究之间的混淆频率，讨论常见的潜在错误并就如何避免这些错误提供建议。2018 年 1 月在六个不同医学领域（心脏病学、临床流行病学、临床神经病学、普通和内科、肾病学和外科）的顶级期刊上发表的观察性队列研究被纳入当前的范围审查。通过信号问题提取关于合并的数据。总共纳入了 180 项研究。全面的，26% (n = 46) 包含病因和预测之间的混淆。合并的频率因医学领域和期刊影响因子而异。因果研究中有 22% 被混为一谈，这主要是由于协变量的选择基于其预测能力而不考虑因果结构。在预测研究中，38% 被混为一谈，最常见的原因是预测模型中包含的协变量的因果解释。病因学和预测的混淆是观察性医学研究中常见的方法学错误，在预测研究中更为常见。由于这可能导致有偏见的估计和错误的结论，研究人员在设计、解释和传播他们的研究时必须小心，以确保避免这种混淆。