The Impact of Extracurricular Surgical Experience on Veterinary Students’ Performance of Canine Ovariohysterectomy and Orchidectomy in a Clinical Skills Curriculum
Bibliographic record
Abstract
Veterinary students may pursue extracurricular surgical experiences before performing ovariohysterectomy or orchidectomy in their veterinary curriculum. We sought to evaluate the impact of these experiences on student confidence and subsequent surgical performance during students’ first canine ovariohysterectomy or orchidectomy during their veterinary school curriculum. We enrolled 69 third-year veterinary students to complete pre- and post-operative surveys reporting their confidence to perform surgery and self-assessing their performance. Students had all completed five semesters of surgical skills training on models and cadavers but varied in their participation in extracurricular surgical experiences. A subset of students ( n = 27) were digitally recorded while performing ovariohysterectomy (16) or orchidectomy (11). Digital recordings were scored by a blinded rater using task-specific rubrics and a global rating scale, and time to perform the procedure was recorded. The number of extracurricular surgeries students had performed was positively correlated with their confidence to perform orchidectomy ( r = .78) but not ovariohysterectomy ( r = −.17). There was no correlation between extracurricular surgeries performed and subsequent rubric scores or surgical times for the first ovariohysterectomy ( r = −.01 and r = −.14, respectively) or orchidectomy ( r = .09 and r = −.18, respectively) performed as part of their veterinary curriculum. Our results suggest that extracurricular surgical experiences may not impart a long-term improvement on performance scores or surgical time during students’ first surgery of their veterinary curriculum. Additional research is necessary to clarify how model training and extracurricular surgical experiences on live animals interact to affect students’ subsequent surgical performance.
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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 | 0.006 |
| 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) | 0.002 | 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".