Teaching Veterinary Anesthesia and Surgery: The Impact of Instructor Availability on Anesthesia, Operative, and Recovery Times in Dogs Undergoing Ovariohysterectomy or Castration
Bibliographic record
Abstract
Veterinary students learning to perform elective ovariohysterectomy (OVH) and castration procedures have traditionally been taught by a few instructors supervising many student groups simultaneously. This study, using a historical control group, explored the impacts of having a dedicated instructor with each student group for anesthetic induction and an entire surgical procedure. Our hypothesis was that preparation, surgical, and anesthesia times would be shorter and, consequently, post-operative recovery times would be shorter with a dedicated instructor compared to traditional methods. Anesthesia records of dogs undergoing elective surgery by third-year veterinary students were reviewed over 3 consecutive years. Traditional instruction was used in year 1 (Y1), and a dedicated instructor per student group was used in year 2 (Y2) and year 3 (Y3). Anesthesia time, surgical time, recovery time, and pre- and post-operative rectal temperature were analyzed, and a stepwise regression model was developed for factors influencing recovery time. Of 206 records reviewed (Y1, 33; Y2, 98; Y3, 75), there were 101 OVH procedures and 105 castration procedures. Preparation, surgery, and anesthesia times were longer in animals undergoing surgery in Y1, when the traditional instruction method was used. Recovery time was not influenced by instructor assignment. Using dedicated instructors to teach OVH and castration to third-year veterinary students decreased overall anesthesia time by 36 to 49 minutes for OVH and 29 to 32 minutes for castration. A teaching model of dedicated instructors requires excellent coordination between surgeons and anesthesiologists to ensure that a similar number of animals can undergo procedures in the time allotted for teaching.
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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 | 0.007 |
| 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".