Evaluation of a First-Year Veterinary Surgical Skills Laboratory: A Retrospective Review
Bibliographic record
Abstract
A retrospective review of the first-year surgical skills competency-based assessment was performed at the Western College of Veterinary Medicine (WCVM) using 6 years of data from 475 students. The cumulative pass rate was 88.2% on first attempt and 99.2% upon remediation. Student gender did not influence overall pass/fail rates, with a failure rate of 11.1% for female students and 10.5% for male students ( p = 0.88). Significantly decreased pass rates were associated with identification of the Mayo scissors (p = 0.03), explanation of using Allis tissue forceps ( p = 0.002), and performance of a Lembert suture pattern ( p < 0.01). An increased pass rate was observed for the cruciate pattern ( p < 0.01). No differences were found in pass/fail rates for hand ties ( p = 0.80) or instrument ties ( p = 0.60). The most common errors occurred with half hitch ties: hand ties (53%) and instrument ties (38%). The most common errors were also recognized for instrument handling (31%) and needle management (20%) during the suture pattern section. The veterinary medical education community may benefit from the evidence-based findings of this research, in terms of understanding student performance across competencies, identifying areas requiring additional mentoring, and determining appropriate competencies for first-year veterinary students.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".