Coumarbatch Jira, Robinson Leah, Thomas Ronald, Bridge Patrick D
Faculty of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI 48201, USA.
Fam Med. 2010 Feb;42(2):105-10.
Failing Step 1 of the US Medical Licensing Examination (USMLE) or a delay in taking the exam can negatively affect a medical student's ability to match into a residency program. Unfortunately, identifying students at risk for failing Step 1 is challenging, but it is necessary to provide proactive educational support. The purpose of this study was to develop a strategy to identify students at risk for failing Step 1.
Using a retrospective study design, 256 students from the class of 2008 were eligible for the study. Independent variables included Medical College Admission Test (MCAT) scores and cumulative grades from years 1--2 of medical school. The dependent variable was their score on the USMLE Step 1. Variables with a significant univariate relationship were loaded into a series of binary logistic regression models. A receiver operating characteristic (ROC) curve examined the significant variables.
Both year-2 standard score and the MCAT biological sciences score were significant as predictors of failure. The ROC curve provided a range of values for establishing a cutoff value for each significant variable.
Using internal and external predictors, it is possible to identify students at risk for failing Step 1 of the USMLE.
美国医师执照考试(USMLE)第一步考试不及格或考试延迟会对医学生进入住院医师培训项目的能力产生负面影响。不幸的是,识别有USMLE第一步考试不及格风险的学生具有挑战性,但提供积极的教育支持是必要的。本研究的目的是制定一种策略来识别有USMLE第一步考试不及格风险的学生。
采用回顾性研究设计,2008届的256名学生符合研究条件。自变量包括医学院入学考试(MCAT)成绩和医学院1至2年级的累积成绩。因变量是他们在USMLE第一步考试中的成绩。具有显著单变量关系的变量被纳入一系列二元逻辑回归模型。受试者工作特征(ROC)曲线对显著变量进行了检验。
二年级标准分数和MCAT生物科学分数都是考试不及格的显著预测因素。ROC曲线为每个显著变量确定临界值提供了一系列数值。
利用内部和外部预测因素,可以识别有USMLE第一步考试不及格风险的学生。