Peri-operative Morbidity Associated with Ovariohysterectomy Performed as Part of a Third-Year Veterinary Surgical-Training Program
Bibliographic record
Abstract
The present study describes the morbidity associated with ovariohysterectomy (OVH) when performed by third-year veterinary students as part of a surgical-training program. Data recorded from medical records included signalment, concurrent illness(es), surgical procedure(s), anesthesia and surgery time, anesthetic and surgical complications, and semester performed. The students' surgical training before the OVH included 39 lecture and 26 laboratory hours. In the present study, 513 animals (206 dogs and 307 cats) were included, of which 120 (23.4%) animals had concurrent illnesses. Median anesthesia time was 145 minutes (ranging from 65 to 240) for cats and 180 minutes (ranging from 90 to 360) for dogs. Median surgery time was 105 minutes (ranging from 50 to 210) for cats and 140 minutes (ranging from 65 to 265) for dogs. There were two (0.4%) major anesthetic complications, one resulting in death. There were 206 (41.7%) minor anesthetic complications, the most common being hypothermia. There were 17 (3.3%) major surgical complications, the most common being body wall dehiscence (n=15), and 49 (9.5%) minor surgical complications, the most common being seroma formation (n=35). Complications were comparable to previous reports. Specific aspects of the program identified for improvement included placing greater emphasis on securely tying the terminal knot of a simple continuous suture pattern to prevent body wall dehiscence, improved measures to reduce post-operative hypothermia, and implementing stricter health screening of animals before enrollment into the program. Faculty program coordinators are encouraged to conduct similar studies so that best practices can be shared and outcomes can be compared as we work toward determining the ideal methods of training students to instill core surgical competencies.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".