Suppr超能文献

理解因果推断:运动损伤预防的未来方向。

Understanding causal inference: the future direction in sports injury prevention.

作者信息

Shrier Ian

机构信息

Centre for Clinical Epidemiology and Community Studies, Lady Davis Institute for Medical Research, Department of Family Medicine, SMBD-Jewish General Hospital, Montréal, Canada.

出版信息

Clin J Sport Med. 2007 May;17(3):220-4. doi: 10.1097/JSM.0b013e3180385a8c.

Abstract

Although physical activity reduces mortality and morbidity, injuries associated with activity may increase both short- and long-term musculoskeletal disability. On the basis of basic science and injury epidemiology studies, authors have made conclusions about cause and effect (causal inferences) and have suggested various interventions to decrease the rate of injuries. However, recent advances in epidemiology suggest that the regression/stratification approach to adjustment for confounding does not provide an appropriate foundation for causal inference; therefore, hypotheses based on traditional analyses may be misleading. The purpose of this article is to provide an overview of the basic concepts of injury epidemiology related to causes, risk factors, and confounding, and to conceptually explain the more recent advances that allow for appropriate interpretations of cause and effect.

摘要

尽管体育活动可降低死亡率和发病率,但与活动相关的损伤可能会增加短期和长期的肌肉骨骼残疾。基于基础科学和损伤流行病学研究,作者们得出了关于因果关系(因果推断)的结论,并提出了各种干预措施以降低损伤发生率。然而,流行病学的最新进展表明,用于调整混杂因素的回归/分层方法并不能为因果推断提供适当的基础;因此,基于传统分析的假设可能会产生误导。本文的目的是概述与原因、风险因素和混杂因素相关的损伤流行病学基本概念,并从概念上解释那些有助于对因果关系进行适当解释的最新进展。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验