Relationships between preclinical course grades and standardized exam performance.
Yinin Hu1, James R Martindale2, Robin D LeGallo3
1Department of Surgery, University of Virginia School of Medicine, P.O. Box 800679, Charlottesville, VA, 22908-0709, USA. yh9b@virginia.edu.
Advances in Health Sciences Education : Theory and Practice
|September 14, 2015
Summary
Performance in pathology, a clinically relevant medical course, strongly predicts success on standardized licensing exams. This finding can guide early interventions for students struggling with medical licensing exams.
Area of Science:
- Medical Education
- Academic Performance Analysis
Background:
- Residency matching success heavily relies on standardized exam scores.
- Identifying predictors of exam performance can inform targeted interventions and curriculum design.
Purpose of the Study:
- To identify preclinical courses that best predict performance on United States Medical Licensing Examination (USMLE) Step exams and National Board of Medical Examiners (NBME) clinical subject exams.
- To evaluate the association between clinically relevant coursework and higher-order cognitive understanding with standardized exam outcomes.
Main Methods:
- Academic data from 795 medical students (2007-2012) were analyzed using multiple linear regression.
- Preclinical course scores were categorized by basic science vs. clinical knowledge focus.
- Medical College Admissions Test (MCAT) scores were included as a covariate.
Main Results:
- Pathology course scores were the strongest predictor of performance on all clinical subject exams and USMLE Step exams, surpassing MCAT scores.
- A pathology score below 75 demonstrated 97% specificity in identifying students scoring below 205 on USMLE Step 1.
- Performance in pathology, a case-based clinical course, significantly correlated with subsequent standardized exam success.
Conclusions:
- Pathology course performance is a significant predictor of medical licensing exam success.
- Clinically relevant, case-based preclinical courses may be valuable for predicting student performance.
- Multiple linear regression effectively identifies courses that can serve as risk stratifiers for standardized exam performance.
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