Comparison of Hemorrhagic Complications with Double-ligated versus Auto-ligated Feline Ovarian Pedicles by Fourth-Year Veterinary Students
Bibliographic record
Abstract
The objective of this article is to compare the occurrence of hemorrhagic complications in student-performed feline ovarian pedicle ligations using the traditional suture pedicle double-ligation (PDL) to the suture-less auto-ligation (AL) techniques, and to describe the stepwise method of teaching the AL technique to students. A total of 287 cats underwent an ovariohysterectomy (OHE) performed by a fourth-year veterinary student trained by veterinary faculty to perform the AL technique beginning with a low-fidelity model and progressing to live patient surgeries. Students performed the AL and PDL techniques on 146 and 141 cats respectively. Hemorrhagic complications occurred in 4 of 146 cats (2.7%) in the AL group and 8 of 141 (5.7%) in the PDL group and were not found to be significantly different ( p = 0.2496). This article demonstrates that novice surgeons can safely perform the AL technique on feline ovarian pedicles without significantly increasing complications compared to the traditionally taught method when a stepwise training program is implemented. Additionally, this technique has been shown to be safe, effective, and more efficient when performed by experienced veterinary surgeons. 1 Veterinary institutions should consider including the AL technique in their core curricula as a standard method for feline ovarian pedicle ligation. Doing so will facilitate the development of more proficient entry-level practitioners who are better able to serve their patients, clients, employers, humane societies, and their communities by using a more efficient and safe feline ovariohysterectomy technique.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".