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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".