Predicting Academic Difficulty in Veterinary Medicine: A Case-Control Study
Bibliographic record
Abstract
A veterinary education is costly and time-consuming, so it is in the best interests of applicants and colleges alike that admissions processes be fair and select applicants who are likely to succeed. We employed a case-control study to explore whether any of 28 admissions variables used by a veterinary college located in the Midwest region of the United States predicted which students would encounter academic difficulty in the veterinary curriculum. Participants were selected from the veterinary classes admitted between 2008 and 2017. We defined academic difficulty cases ( n = 55) as any students dismissed from the program or placed on academic probation. Controls ( n = 220) were selected at random from the same classes, excluding the academic difficulty cases, students with honor code violations, or any who had exited the program early for any reason other than participation in a concurrent program. Admissions variables included gender, citizenship, underrepresented status, state of residence, age, interview scores, GPA (science), GRE scores, undergraduate credits, participation in honors courses, community college credits, repeats/withdrawals of required undergraduate courses, course load, and admissions committee review criteria including work experience, animal/vet experience, references, essays, leadership, personal development, special circumstances, and overall committee score. Zero-order correlations for academic difficulty were significant for underrepresented status, age, GPA (science), verbal and quantitative GRE scores, repeats/withdrawals, and references. When combined in logistic regression, only science GPA, verbal GRE, and references significantly and independently predicted struggler status.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.007 | 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".