Teaching Surgery to the Veterinary Novice: The Ohio State University Experience
Bibliographic record
Abstract
Surgical training in veterinary medicine has evolved rapidly over the past several decades. Catalysts for change include pressure from concerned students and the public to reduce the use of live animals in teaching; less-than-effective preparation of students for live surgery experience; an overall reduction in faculty time and effort devoted to skills training; college budgetary reallocations mandating reductions in expensive group laboratory experiences; and more specialized case-load patterns in clinical rotations, which have reduced students' exposure to common surgical conditions. In response to these trends, methods for surgery educators to reduce, refine, and replace live animals in surgery training courses at veterinary schools have received broad attention. When these methods are used effectively in a curriculum, it is no longer necessary to sacrifice animals for adequate student training. This article describes a successful and ethical surgical training program used at the Ohio State University College of Veterinary Medicine (OSU-CVM). This program provides early exposure to skills training using surgical simulators and auto-tutorials, ensures that basic skills are mastered before students are exposed to cadaver practice, and requires application of model-based skills to cadavers, with final matriculation to intensive exposure to multiple live-animal procedures via a collaborative surgery program with a local shelter.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".