The Effect of an Ovariohysterectomy Model Practice on Surgical Times for Final-Year Veterinary Students’ First Live-Animal Ovariohysterectomies
Bibliographic record
Abstract
This study evaluated whether one supervised simulated ovariohysterectomy (OVH) using a locally developed canine OVH model, decreased surgical time for final-year veterinary students’ first live-animal OVH. We also investigated student perceptions of the model as a teaching aid. Final-year veterinary students were exposed to an OVH model (Group M, n = 48) and compared to students without the exposure (Group C, n = 58). Both groups were instructed similarly on performing an OVH using a lecture, student notes, a video, and a demonstration OVH performed by a veterinary surgeon. Students in Group M then performed an OVH on the model before performing a live-animal OVH. Students in Group C had no exposure to the OVH model before performing a live-animal OVH. Surgical time data were analyzed using linear regression. Students in Group M completed a questionnaire on the OVH model after performing their first live-animal OVH. The OVH model exposure reduced students’ first canine live-animal OVH surgery time ( p = .009) for students without prior OVH experience. All students ( n = 48) enjoyed performing the procedure on the mode; students practicing an OVH on the model felt more confident (92%) and less stressed (73%) when performing their first live-animal OVH. Results suggest that the canine OVH model may be helpful as a clinical training tool and we concluded that the OVH model was effective at decreasing students’ first OVH surgical time.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".