Veterinarian Use of and Sentiment Regarding Standard of Care for Feline Ovarian Pedicle Tie
Bibliographic record
Abstract
Ligation of the feline ovarian pedicle is commonly performed via autoligation ("pedicle tie") by high-quality high-volume surgeons. It is not commonly taught in veterinary school, resulting in general practice veterinarians who are not comfortable with the technique, despite being faster than double ligation with suture, having very low risk of hemorrhage-related complications, and no increased risk for student surgeons. This study aimed to determine the use of and opinions regarding standard of care (SOC) related to the pedicle tie for feline ovariohysterectomy. An anonymous 23-question survey was targeted to private practice veterinarians via social media from September to November 2021. There were 142 respondents, with 77 indicating the use of the pedicle tie and 65 indicating no use. Of those who used the pedicle tie, most reported learning the technique from either a dedicated high-quality high-volume facility (32%) or a colleague/mentor (32%). Nearly half used it in all cases. They had used it for a median of 5 (IQR 3,8) years, and 99% were comfortable teaching the technique to others. Of those who did not use the pedicle tie, 60% reported that it meets SOC, 14% reported that it does not, and 26% reported that it sometimes meets SOC. Over half of the respondents reported using the pedicle tie. Absence from the veterinary curriculum was the most frequently cited reason for not using the 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".