Brief Communication: Predictive Value of Veterinary Student Application Data for Performance in Clinical Year 4
Bibliographic record
Abstract
Student application packages for admission to the University of Georgia College of Veterinary Medicine currently include the following information: undergraduate grade point average (GPA), GPA in science courses, GPA in non-science courses, GPA for the last 45 hours (GPALast45hrs), Graduate Record Examination Quantitative and Verbal Reasoning (GRE-QV) score, GRE Analytical Writing (GRE-AW) score, and grades for 10 required prerequisite courses. From these data, an academics score of up to 70 points is calculated. Faculty reviewers also score each applicant up to a maximum of 30 points (FileScore), giving a total possible score of 100 points. Previous analyses demonstrated that the file score and academic variables are significantly related to first-year GPA of veterinary students; however, it is unknown how these variables relate to performance in clinical rotations. The present study pooled the two most recent graduating classes to compare each academic score component to student clinical rotation grades received during year 4 (CGrYr4) in the teaching hospital. Only one component of the student application packages-the pre-admission GRE-QV score-significantly correlated with CGrYr4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".