Department of Surgery, Section of Gastrointestinal Surgery, University of Alabama at Birmingham, Birmingham, Alabama.
The University of Alabama, College of Community Health Sciences, Tuscaloosa, Alabama.
J Surg Educ. 2018 Jul-Aug;75(4):846-853. doi: 10.1016/j.jsurg.2017.09.012. Epub 2017 Oct 9.
This study aimed to identify medical student characteristics that predict a successful categorical match into a general surgery residency and a match based upon Doximity program rankings.
This was a retrospective study that analyzed academic and personal predictors of a successful general surgery residency match.
This study was set at the University of Alabama at Birmingham School of Medicine, a public medical school.
This study included 173 fourth-year medical students at a public medical school who matched into general surgery residency programs.
Our cohort comprised students graduating from our institution between 2004 and 2015 that matched into preliminary or categorical general surgery positions. We collected academic variables and performed univariate analyses and logistic regression to examine the likelihood of specific match outcomes.
Of 173 students, 132 (76%) matched into a categorical position and 41 (24%) matched into a preliminary position. Of all variables, clinical ranking quartile was most effective in predicting a categorical match (R = 0.35). Models for a match based upon Doximity ranking lacked the same predictive power.
This research identifies students that are at risk for not matching into a categorical position and predicts competitiveness for certain programs. It provides a novel calculator to give applicants easily interpretable match probabilities.
本研究旨在确定医学生的特征,这些特征可以预测他们在普通外科住院医师培训中获得成功的分类匹配,以及根据 Doximity 计划排名获得匹配的概率。
这是一项回顾性研究,分析了学术和个人预测因素对普通外科住院医师培训匹配成功的影响。
本研究在阿拉巴马大学伯明翰医学院进行,这是一所公立医学院。
本研究包括 173 名在公立医学院就读的四年级医学生,他们都成功匹配到普通外科住院医师培训项目。
我们的队列包括 2004 年至 2015 年期间从我们机构毕业的学生,他们匹配到了初步或分类普通外科职位。我们收集了学术变量,并进行了单变量分析和逻辑回归,以检查特定匹配结果的可能性。
在 173 名学生中,有 132 名(76%)学生匹配到了分类职位,41 名(24%)学生匹配到了初步职位。在所有变量中,临床排名四分位距最能有效预测分类匹配(R = 0.35)。基于 Doximity 排名的匹配模型缺乏相同的预测能力。
这项研究确定了那些有可能无法匹配到分类职位的学生,并预测了某些项目的竞争力。它提供了一个新颖的计算器,可以为申请人提供易于理解的匹配概率。