Canadian veterinarians’ use of analgesics in cattle, pigs, and horses in 2004 and 2005
Bibliographic record
Abstract
Anecdotal evidence suggests that many veterinarians may not use analgesics in livestock for routine surgical procedures or painful disease states. To investigate this, we conducted a national mail survey of a random sample of 1431 Canadian veterinarians (response rate, 50.1%). Questions primarily concerned veterinarians’ analgesic usage for common surgeries and medical conditions in beef and dairy cattle, pigs, and horses, and attitudes toward pain management. More than 90% of veterinarians used analgesic drugs for equine surgeries, for cesarean section in sows and cows, and for bovine claw amputation and omentopexy. However, in these and other categories, the analgesics used were often inadequate, and many veterinarians did not give analgesics to young animals. When castrated, < 0.001% of piglets received analgesia, compared with 6.9% of beef calves and 18.7% of dairy calves ≤ 6 mo of age, 19.9% of beef calves and 33.2% of dairy calves > 6 mo of age, and 95.8% of horses. Respondents largely agreed that there are no long-acting, cost-effective analgesics available for use in livestock (median rating 8/10; interquartile range 4–9), and that the long or unknown withdrawal periods of some drugs outweighed the benefits of using them (median rating 7/10; interquartile range 4–9). The results indicate an urgent need for veterinarians to manage pain in livestock better. Continuing education would help, as would an increase in the number of approved, cost-effective analgesic drugs with known withdrawal periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".