Analgesic practices for acute pain management in cats and dogs in Africa
Bibliographic record
Abstract
OBJECTIVES: To understand perceptions and analgesic practices for acute pain management in cats and dogs by veterinarians in Africa. MATERIALS AND METHODS: Data from small animal veterinarians were collected using an online questionnaire (English/French) and convenience sampling. Pain management practices and perceptions of veterinarians were analysed using multiple correspondence analysis. RESULTS: A total of 249 participants completed the survey from 20 countries. The readily available opioids, non-steroidal anti-inflammatory drugs and local anaesthetics were morphine (n = 90; 36.1%), meloxicam (n = 200; 80.3%) and lidocaine (n = 245; 98.4%), respectively. The majority of participants reported not using pain assessment instruments for perioperative pain in cats (n = 169; 67.9%) or dogs (n = 170; 68.3%). Feline Grimace Scale (n = 27; 10.8%) and Glasgow Composite Measure Pain Scale (n = 30; 12.0%) were the most commonly used instruments in cats and dogs, respectively. Multiple correspondence analysis identified one cluster representing high knowledge of pain assessment, use of peri-operative non-steroidal anti-inflammatory drugs and opioids, pain assessment instruments and recommendations of ongoing non-steroidal anti-inflammatory drug therapy after ovariohysterectomy. The second cluster represented low knowledge of pain assessment, non-use of peri-operative non-steroidal anti-inflammatory drugs and opioids, non-use of pain assessment instruments and no recommendations for ongoing non-steroidal anti-inflammatory drug therapy after ovariohysterectomy. CLINICAL SIGNIFICANCE: Low knowledge and limited access to analgesics were associated with the non-use of perioperative non-steroidal anti-inflammatory drugs and opioids, pain assessment tools and non-steroidal anti-inflammatory drug therapy following ovariohysterectomy. Continuing education programs and improved drug availability are essential for improving pain management in cats and dogs.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| 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".