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 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 | 0.000 | 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) | 0.002 | 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".