The Validity of MCAT Scores in Predicting Students’ Performance and Progress in Medical School: Results From a Multisite Study
Bibliographic record
Abstract
PURPOSE: This is the first multisite investigation of the validity of scores from the current version of the Medical College Admission Test (MCAT) in clerkship and licensure contexts. It examined the predictive validity of MCAT scores and undergraduate grade point averages (UGPAs) for performance in preclerkship and clerkship courses and on the United States Medical Licensing Examination Step 1 and Step 2 Clinical Knowledge examinations. It also studied students' progress in medical school. METHOD: Researchers examined data from 17 U.S. and Canadian MD-granting medical schools for 2016 and 2017 entrants who volunteered for the research and applied with scores from the current MCAT exam. They also examined data for all U.S. medical schools for 2016 and 2017 entrants to regular-MD programs who applied with scores from the current exam. Researchers conducted linear and logistic regression analyses to determine whether MCAT total scores added value beyond UGPAs in predicting medical students' performance and progress. Importantly, they examined the comparability of prediction by sex, race and ethnicity, and socioeconomic status. RESULTS: Researchers reported medium to large correlations between MCAT total scores and medical student outcomes. Correlations between total UGPAs and medical student outcomes were similar but slightly lower. When MCAT scores and UGPAs were used together, they predicted student performance and progress better than either alone. Despite differences in average MCAT scores and UGPAs between students who self-identified as White or Asian and those from underrepresented racial and ethnic groups, predictive validity results were comparable. The same was true for students from different socioeconomic backgrounds, and for males and females. CONCLUSIONS: These data demonstrate that MCAT scores add value to the prediction of medical student performance and progress and that applicants from different backgrounds who enter medical school with similar ranges of MCAT scores and UGPAs perform similarly in the curriculum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".