Wilkerson Gary B, Denegar Craig R
Graduate Athletic Training Education Program, University of Tennessee-Chattanooga; †Department of Kinesiology, University of Connecticut, Storrs.
J Athl Train. 2014 Jul-Aug;49(4):561-7. doi: 10.4085/1062-6050-49.3.43. Epub 2014 Jun 16.
Providing patient-centered care requires consideration of numerous factors when making decisions that will influence a patient's health status.
Clinical decisions should be informed by relevant research evidence, but the literature often lacks pertinent information for problems encountered in routine clinical practice. Although a randomized clinical trial provides the best research design to ensure the internal validity of study findings, ethical considerations and the competitive culture of sport often preclude random assignment of patients or participants to a control condition.
A cohort study design and Bayesian approach to data analysis can provide valuable evidence to support clinical decisions. Dichotomous classification of both an outcome and 1 or more predictive factors permits quantification of the likelihood of occurrence of a specified outcome.
Multifactorial prediction models can reduce uncertainty in clinical decision making and facilitate the individualization of treatment, thereby supporting delivery of clinical services that are both evidence based and patient centered.
提供以患者为中心的护理需要在做出影响患者健康状况的决策时考虑众多因素。
临床决策应以相关研究证据为依据，但文献往往缺乏常规临床实践中遇到问题的相关信息。尽管随机临床试验提供了确保研究结果内部有效性的最佳研究设计，但伦理考量和体育界的竞争文化常常排除将患者或参与者随机分配到对照条件下的可能性。
队列研究设计和贝叶斯数据分析方法可为支持临床决策提供有价值的证据。对结果和一个或多个预测因素进行二分分类可量化特定结果发生的可能性。
多因素预测模型可减少临床决策中的不确定性并促进治疗个体化，从而支持提供基于证据且以患者为中心的临床服务。
