Management of veterinary anaesthesia and analgesia in small animals: A survey of English-speaking practitioners in Canada
Bibliographic record
Abstract
OBJECTIVE: To describe how small animal anaesthesia and analgesia is performed in English-speaking Canada, document any variation among practices especially in relation to practice type and veterinarian's experience and compare results to published guidelines. DESIGN: Observational study, electronic survey. SAMPLE: 126 respondents. PROCEDURE: A questionnaire was designed to assess current small animal anaesthesia and analgesia practices in English-speaking Canadian provinces, mainly in Ontario, Alberta and British Columbia. The questionnaire was available through SurveyMonkey® and included four parts: demographic information about the veterinarians surveyed, evaluation and management of anaesthetic risk, anaesthesia procedure, monitoring and safety. Year of graduation and type of practice were evaluated as potential risk factors. Exact chi-square tests were used to study the association between risk factors and the association between risk factors and survey responses. For ordinal data, the Mantel-Haenszel test was used instead. RESULTS: Response rate over a period of 3 months was 12.4% (126 respondents out of 1 016 invitations). Current anaesthesia and analgesia management failed to meet international guidelines for a sizable number of participants, notably regarding patient evaluation and preparation, safety and monitoring. Nearly one third of the participants still consider analgesia as optional for routine surgeries. Referral centres tend to follow guidelines more accurately and are better equipped than general practices. CONCLUSIONS AND CLINICAL RELEVANCE: A proportion of surveyed Canadian English-speaking general practitioners do not follow current small animal anaesthesia and analgesia guidelines, but practitioners working in referral centres are closer to meet these recommendations.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".