Perceptions and opinions of Canadian pet owners about anaesthesia, pain and surgery in small animals
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the perceptions and opinions of Canadian pet owners about anaesthesia, pain and surgery in dogs and cats. METHODS: Six Canadian veterinary hospitals participated. Each practice received 200 copies of a questionnaire that were distributed to pet owners. Questions regarding the use of analgesics, anaesthesia, surgery and onychectomy (cats) were included. Responses were transformed into ordinal scores and analysed with a Cochran-Mantel-Haenszel test. RESULTS: A total of 849 out of 1200 questionnaires were returned. Owners believed more frequently that analgesics are needed for surgical procedures than for the medical conditions. Owners rated as very important/important: "knowing what to expect during illness/injury/surgery" (99·3%), "being assured that all necessary analgesic drugs/techniques will be used" (98·6%), "being informed about procedures/risk" (98·5%), and having a board-certified anaesthesiologist (90·5%). Most owners agreed/partly agreed that pain impacts quality of life (94·2%), and affects their pet's behaviour (89·5%). Most respondents (69%) were women; they were significantly more concerned than men about anaesthesia, pain, cost and client-communication. Cat owners believed that analgesics were necessary for some procedures/conditions significantly more often than canine-only owners. Pet owners with previous surgery disagreed more frequently that "pain after surgery can be helpful" and that "pain in animals is easy to recognize" than those without previous surgery. Most owners think onychectomy should be banned in cats (56·4%). CLINICAL SIGNIFICANCE: This study identified important areas of client communication regarding pain and its control in pets.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".