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Background: Medical education directly impacts patient care, yet the predictive validity of pre-clinical academic performance for licensure exam outcomes remains debated. This national, multi-institutional study (2019-2021) assessed the relationship between university course grades, cumulative grade point average (GPA), and Comprehensive Basic Science Examination (CBSE) scores in Iranian medical students. Methods: Course grades and GPAs of 23 medical schools were linked to CBSE outcomes of 51 medical schools across five consecutive exam periods via student national ID. Pearson's correlation, paired t-tests, ANOVA, and chi-square assessed trends. Hierarchical cluster analysis (dendrogram) examined course grade correlations. Independent CBSE total score predictors were found using multiple linear regression. Results: Of the 25,757 individual records, 9,359 (45.2% female) had complete academic and CBSE data, making them eligible for primary analyses (84.5% passed CBSE on the first attempt). The GPA was 15.11±1.74, and the CBSE score was 101.68±24.61. All course grades correlated significantly with CBSE subtests (r=0.055-0.544, P<0.001). A significant moderate association (r=0.492, P<0.001) exists between overall GPA and CBSE. Repeat examinees had considerably lower GPAs and CBSE scores (P<0.001). GPA (β=0.318), Anatomy (β=0.158), Physiology (β=0.135), Epidemiology (β=0.043), and Virology (β=0.043) were the most significant predictors in regression modeling (R²=0.426). Cluster analysis showed that academic grades in anatomy, physiology, and biochemistry were strongly correlated with CBSE subtests. Conclusion: This study represents the first large-scale national dataset in Iran pertaining to medical education. Pre-clinical GPA and course grades exhibit overall and subject-specific, notable predictive validity for CBSE performance. To enhance medical education and licensure results, it is advisable to implement standardized, cross-institutional comparisons alongside dynamic curriculum reviews. The regression model and clustering insights provide a framework for targeted educational interventions.
Rashidi et al. (Mon,) studied this question.