PERFORMANCE IN PATHOPHYSIOLOGY AS A PREDICTOR OF THE OUTCOME OF THE “KROK 1” LICENSING EXAM
DOI:
https://doi.org/10.11603/m.2414-5998.2026.3.16632Keywords:
pathophysiology; “KROK 1” licensing examination; academic performance; predictive model; medical education; performance predictorsAbstract
This article examines and analyzes the academic performance of medical students in the course “Pathophysiology” in relation to their results on the “KROK 1” licensing exam, under the reformed exam framework implemented in 2026, which led to an increase in the exam’s difficulty. The aim of the article is to conduct a comparative analysis of performance indicators in pathophysiology and “KROK 1” results across sociodemographic and academic subgroups of higher education students, to assess the relationship between current and semester-based academic performance and the percentage of correct answers on the exam, as well as to develop a multifactorial predictive model for passing the exam. To achieve this objective, the following set of methods was applied: retrospective observational analysis of data from 763 third-year medical students; statistical methods (Shapiro-Wilk test, Mann-Whitney U test, Fisher’s exact test, Spearman’s correlation analysis); univariate and multivariate linear regression modeling with stepwise variable selection using Wald’s criterion. It was found that, despite a statistically significant advantage for women in terms of scores on the ongoing assessment and the first stage of the exam, no gender differences were detected at the “KROK 1” level; the educational background at the time of admission influenced performance only at the level of pairwise comparisons, losing its independent predictive value in the multivariate model; in contrast, the status of applicants readmitted to the program demonstrated consistently lower performance across all components of the discipline and the highest rate of failure on “KROK 1”. It was concluded that success on “KROK 1” is determined primarily by applicants’ academic performance in pathophysiology, rather than by formal sociodemographic characteristics, with the status of a reinstated applicant emerging as the strongest and most independent predictor of academic risk. The study substantiates the feasibility of using a four-factor regression model – which accounts for scores on ongoing assessments, both stages of the semester exam, and reinstatement status – as a tool for the early identification of students at academic risk and for planning individualized support within the educational process.
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