Evaluation of Veterinary Student Surgical Skills Preparation for Ovariohysterectomy Using Simulators: A Pilot Study
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes the development and evaluation of training intended to enhance students' performance on their first live-animal ovariohysterectomy (OVH). Cognitive task analysis informed a seven-page lab manual, 30-minute video, and 46-item OVH checklist (categorized into nine surgery components and three phases of surgery). We compared two spay simulator models (higher-fidelity silicone versus lower-fidelity cloth and foam). Third-year veterinary students were randomly assigned to a training intervention: lab manual and video only; lab manual, video, and $675 silicone-based model; lab manual, video, and $64 cloth and foam model. We then assessed transfer of training to a live-animal OVH. Chi-square analyses determined statistically significant differences between the interventions on four of nine surgery components, all three phases of surgery, and overall score. Odds ratio analyses indicated that training with a spay model improved the odds of attaining an excellent or good rating on 25 of 46 checklist items, six of nine surgery components, all three phases of surgery, and the overall score. Odds ratio analyses comparing the spay models indicated an advantage for the $675 silicon-based model on only 6 of 46 checklist items, three of nine surgery components, and one phase of surgery. Training with a spay model improved performance when compared to training with a manual and video only. Results suggested that training with a lower-fidelity/cost model might be as effective when compared to a higher-fidelity/cost model. Further research is required to investigate simulator fidelity and costs on transfer of training to the operational environment.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it