Factors affecting the use of postincisional analgesics in dogs and cats by Canadian veterinarians in 2001.
Bibliographic record
Abstract
Factors affecting the postincisional use of analgesics for ovariohysterectomy (OVH) in dogs and cats were assessed by using data collected from 280 Canadian veterinarians, as part of a national, randomized mail survey (response rate 57.8%). Predictors of analgesic usage identified by logistic regression included the presence of at least 1 animal health technician (AHT) per 2 veterinarians (OR = 2.3, P = 0.004), and the veterinarians' perception of the pain caused by surgery without analgesia (OR = 1.5, P < 0.001). Linear regression identified the following predictors of veterinarians' perception of pain: the presence of more than 1 AHT per 2 veterinarians (coefficient = 0.42, P = 0.048) and the number of years since graduation (coefficient = -0.073, P < 0.001). Some of these risk factors are similar to those identified in 1994. The results suggest that continuing education may help to increase analgesic usage. Other important contributors may be client education and a valid method of pain assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".