An Anatomy Pre-Course Predicts Student Performance in a Professional Veterinary Anatomy Curriculum
Bibliographic record
Abstract
Little to no correlation has been identified between previous related undergraduate coursework or outcomes on standardized tests and performance in a veterinary curriculum, including anatomy coursework. Therefore, a relatively simplistic method to predict student performance before entrance would be advantageous to many. The purpose of this study was to evaluate whether there is a correlation between performance in a veterinary anatomy pre-course and subsequent performance within a professional anatomy curriculum. Incoming first-year veterinary students at the Louisiana State University School of Veterinary Medicine were asked to participate in a free weeklong pre-course, before the start of the semester. The pre-course covered the musculoskeletal anatomy of the canine thoracic limb using dissection-based methods. Student performance, as evaluated by test grades in the pre-course, did indeed correlate with test grades in professional veterinary anatomy courses. A significant and positive correlation was identified between pre-course final exam performance and performance on examinations in each of 3 professional anatomy courses. Qualitative analyses of student comments pertaining to their experience within the pre-course indicated differences in the perceived benefits of the pre-course between high-, middle-, and low-performing students. These varied perceptions may provide predictive feedback as well as guidance for supporting lower performing students. Together, these results indicate that performance in a weeklong pre-course covering only a small portion of canine anatomy is a strong predictor of performance within a professional anatomy curriculum. In addition, the pre-course differentially affected student perceptions of their learning experience.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.008 | 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".