Hu Yinin, Martindale James R, LeGallo Robin D, White Casey B, McGahren Eugene D, Schroen Anneke T
Department of Surgery, University of Virginia School of Medicine, P.O. Box 800679, Charlottesville, VA, 22908-0709, USA.
Undergraduate Medical Education, University of Virginia School of Medicine, Charlottesville, VA, USA.
Adv Health Sci Educ Theory Pract. 2016 May;21(2):389-99. doi: 10.1007/s10459-015-9637-6. Epub 2015 Sep 12.
Success in residency matching is largely contingent upon standardized exam scores. Identifying predictors of standardized exam performance could promote primary intervention and lead to design insights for preclinical courses. We hypothesized that clinically relevant courses with an emphasis on higher-order cognitive understanding are most strongly associated with performance on United States Medical Licensing Examination Step exams and National Board of Medical Examiners clinical subject exams. Academic data from students between 2007 and 2012 were collected. Preclinical course scores and standardized exam scores were used for statistical modeling with multiple linear regression. Preclinical courses were categorized as having either a basic science or a clinical knowledge focus. Medical College Admissions Test scores were included as an additional predictive variable. The study sample comprised 795 graduating medical students. Median score on Step 1 was 234 (interquartile range 219-245.5), and 10.2 % (81/795) scored lower than one standard deviation below the national average (205). Pathology course score was the strongest predictor of performance on all clinical subject exams and Step exams, outperforming the Medical College Admissions Test in strength of association. Using Pathology score <75 as a screening metric for Step 1 score <205 results in sensitivity and specificity of 37 and 97 %, respectively, and a likelihood ratio of 11.9. Performance in Pathology, a clinically relevant course with case-based learning, is significantly related to subsequent performance on standardized exams. Multiple linear regression is useful for identifying courses that have potential as risk stratifiers.
住院医师匹配的成功很大程度上取决于标准化考试成绩。识别标准化考试成绩的预测因素可以促进早期干预,并为临床前课程带来设计思路。我们假设,强调高阶认知理解的临床相关课程与美国医学执照考试第一阶段考试和美国国家医学考试委员会临床科目考试的成绩关联最为紧密。收集了2007年至2012年间学生的学术数据。临床前课程成绩和标准化考试成绩用于多元线性回归的统计建模。临床前课程分为侧重于基础科学或临床知识两类。医学院入学考试成绩作为一个额外的预测变量纳入其中。研究样本包括795名即将毕业的医学生。第一阶段考试的中位数成绩为234分(四分位距为219 - 245.5),10.2%(81/795)的学生成绩低于全国平均水平一个标准差(205分)。病理学课程成绩是所有临床科目考试和第一阶段考试成绩的最强预测因素,其关联强度超过医学院入学考试。将病理学成绩<75作为第一阶段考试成绩<205的筛选指标,敏感性和特异性分别为37%和97%,似然比为11.9。病理学这门基于案例学习的临床相关课程的成绩与后续标准化考试成绩显著相关。多元线性回归有助于识别具有作为风险分层指标潜力的课程。