Can we mitigate pain associated with castration in beef cattle at early (<6 months of age) ages? A review from a Canadian perspective
Bibliographic record
Abstract
The objective of this paper was to review the literature regarding castration method, age, and pain mitigation strategies assessed in young beef calves under 6-months-of-age, summarize study findings and provide guidance regarding best practice for controlling pain in young calves. Collectively, research shows that knife castration resulted in a greater number and magnitude of acute indicators of pain lasting up to 7 days, while banding caused both acute and longer-term pain associated with inflammation lasting several weeks. In addition, calves <2-months-of-age generally healed faster, had reduced physiological responses, and had better growth recovery post-castration compared to calves between 2 and <6-months-of-age. Administration of a local anesthetic in combination with an analgesic effectively reduced behavioural and physiological indicators of pain. Given that less pain was observed in younger calves, knife castration resulted in shorter-term pain with faster healing than banding, and pain control is more easily achieved for acutely painful procedures; knife castration soon after birth (≤2-months-of-age), conducted using pain control is recommended. Future studies should focus on comparing the effects of varying castration techniques within castration methods as well as drug delivery and timing techniques that could further improve pain control strategies in castrated calves.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".