Anesthetic and analgesic management of cats undergoing elective neutering: Survey of practices and opinions of veterinarians in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To determine the anesthetic and analgesic protocols and techniques used in cats undergoing elective neutering in Ontario, Canada and to obtain veterinarians' opinions regarding their choices. STUDY DESIGN: Cross-sectional survey. ANIMALS: Client-owned cats undergoing elective neutering in Ontario, Canada. METHODS: A confidential mixed-mode survey about anesthetic and analgesic management practices used in client-owned cats undergoing elective neutering was distributed to veterinarians (n = 2921) working in companion animal practice in Ontario. Descriptive statistics were generated. Logistic regression was used to identify associations between demographic factors and protocols. Chi-square analysis was used to compare protocols used in cats undergoing ovariohysterectomy (OVH) versus castration. RESULTS: Four hundred and thirty-one individuals (14.8%) completed the survey. Most respondents used a sedative before induction of anesthesia (OVH: 368/387; 95.1% and castration 360/381; 94.5%) and gave an opioid and/or a non-steroidal anti-inflammatory drug perioperatively (OVH: 379/381; 99.7% and castration: 379/382; 99.2%). Respondents placed an intravenous catheter and performed orotracheal intubation more frequently in cats undergoing OVH (catheter: 366/387; 94.6% and intubation: 379/386; 98.2%) compared with castration (catheter: 219/380; 57.6% and intubation: 166/375; 44.3%) (p < 0.001). Respondents more closely followed current anesthesia guidelines relative to their peers if they: graduated during or after 2000, were women, performed 1-10 OVHs per week, worked in an urban setting, in a companion animal practice, with three or more veterinarians or three to five registered veterinary technicians. Most respondents were very satisfied or satisfied with their current anesthetic drug protocol for cats undergoing elective OVH (396/412; 96.1%) and castration (386/413; 93.4%). CONCLUSIONS AND CLINICAL RELEVANCE: Most veterinarians in Ontario who participated in the survey follow many of the current guidelines regarding anesthetic and analgesia management practices for cats undergoing elective neutering. Anesthetic protocols varied with respondent demographics; however, most respondents were satisfied with their choices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".