Attitudes and opinions of Brazilian veterinarians towards the assessment and management of acute avian pain
Bibliographic record
Abstract
BACKGROUND: Veterinarians' approaches to the management of avian pain have been poorly documented despite the rising number of pet birds seen in clinical settings. METHODS: An online survey was advertised nationwide to recruit Brazilian veterinarians who had treated traumatic and surgical conditions in birds within the previous year. The survey comprised 25 closed or semi-closed questions divided into four sections (demographics, routinely performed medical procedures and pain recognition, drug choices for analgesia and challenges to pain treatment, and attitudes towards pain relief in birds). Survey results are expressed as a percentage of responses and a chi-squared test was used to compare proportions. RESULTS: A total of 370 completed surveys were received. Approximately 72% of respondents worked exclusively in wild/exotic animal practice. Parrots and related species were the most commonly seen birds. The most frequently reported painful conditions were fractures (88.4%), feather plucking (73.0%) and limb amputation (65.1%). Although pain was diagnosed behaviourally by 97.6% of the respondents, 83.5% believed that the presence of an observer inhibited avian pain expression. NSAIDs and opioids, most commonly meloxicam and tramadol, were always provided perioperatively by 66.4% and 42.1% of respondents, respectively. Although nearly all respondents (95.4%) agreed that analgesics improve the quality of recovery after surgery, 80.3% stated that acute pain in birds is frequently undiagnosed in the clinical setting. LIMITATIONS: Selection bias could have overestimated the attitudes concerning avian pain in relation to the wider veterinary population. CONCLUSION: Practising veterinarians in Brazil revealed a positive attitude towards avian pain management. However, the lack of validated pain assessment methods makes avian pain relief highly challenging.
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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 | 0.001 | 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".