# Predictors of Academic and Non-Academic Termination From Medical Schools

**Authors:** Kenneth D Royal, Andrea Carpentieri, Robert Santos, David Stenulson

PMC · DOI: 10.7759/cureus.98889 · Cureus · 2025-12-10

## TL;DR

This study explores whether admissions metrics can predict medical students who are likely to be terminated for academic or non-academic reasons.

## Contribution

The study identifies that academic and professional readiness metrics can predict different types of student termination outcomes.

## Key findings

- Students who withdrew academically had significantly lower MCAT and uGPA scores compared to the baseline group.
- Non-academic dismissals were strongly predicted by lower PREview scores and moderately by lower uGPA.
- Academic dismissions showed large effect sizes for MCAT and uGPA differences compared to the baseline.

## Abstract

Background and objective

Termination from medical school is relatively uncommon but typically results from either withdrawal or dismissal. Although the number of individuals terminated for academic and non-academic reasons annually is quite small, the collective impact is substantial and has significant implications for students, medical schools, public health, and the physician workforce. While some types of termination may be impossible to predict, those linked to academic performance or non-academic factors such as professionalism may be more predictable. This study aimed to examine how admissions metrics compare between medical students terminated for academic or non-academic reasons versus a baseline group consisting of all other matriculating students and recent graduates.

Methods

This study examined aggregate national data for student cohorts matriculating into traditional MD-granting degree programs during the 2021-2024 academic years. We examined only those students who 1) withdrew for academic reasons; 2) were dismissed for academic reasons; or 3) were dismissed for non-academic reasons. Scores from three common admissions metrics (Medical College Admission Test (MCAT®) scores, undergraduate grade point average (uGPA), the Association of American Medical Colleges (AAMC) PREview® Professional Readiness Exam) were compared between terminated students and the baseline group during this period.

Results

The 169 students who withdrew for academic reasons had significantly lower MCAT scores and uGPAs than the baseline group. The differences in MCAT scores resulted in a large effect size, whereas differences in uGPA led to a medium effect size. The differences in PREview scores, although not statistically significantly different, did present a medium effect size. The 193 students dismissed for academic reasons also had significantly lower median MCAT scores and uGPAs than the baseline group, and the contrast in scores had large effect sizes for both measures. The PREview scores were identical between the academically dismissed and baseline groups, indicating no meaningful difference. The 42 students dismissed for non-academic reasons had significantly lower PREview scores than did the baseline group, with a large effect size. uGPA was also significantly lower in the non-academically dismissed group and had a medium effect size. MCAT scores were comparable between these groups.

Conclusions

Medical student terminations have costly repercussions for a variety of constituents. To help mitigate termination outcomes, we sought to understand if common admissions metrics may be associated and thus potentially serve as useful predictors. Based on the preliminary evidence presented in this study, we conclude that metrics of academic readiness, such as MCAT scores and uGPA, are strong predictors of academic withdrawal and academic dismissal outcomes, whereas PREview scores, a metric of professional readiness, are a strong predictor of non-academic dismissal.

## Full-text entities

- **Diseases:** anxiety (MESH:D001007), MD (MESH:C535955), burnout (MESH:D002055)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## References

12 references — full list in the complete paper: https://tomesphere.com/paper/PMC12785480/full.md

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Source: https://tomesphere.com/paper/PMC12785480