Efficacy of pain management for cattle castration: A systematic review and meta-analysis
Bibliographic record
Abstract
Much research has assessed methods of pain control for cattle castration, but there remains a lack of consensus regarding best practice. We conducted a systematic review and meta-analysis of published research including both an untreated control (i.e. castrated without pain mitigation) and at least one unimodal or multimodal analgesia treatment (i.e. castrated with a local anaesthetic alone, or in combination with a non-steroidal anti-inflammatory drug) to summarise findings on castration pain management. Studies were included if they castrated by surgery, elastration or crushing, and reported at least one of the following outcomes: cortisol, change in bodyweight, foot stomping, wound licking, a subjective assessment of pain using a visual analogue scale, or stride length. Our search identified 383 publications, of which 17 were eligible for inclusion. Most publications focused on surgical castration (n = 14), and the most frequently reported outcome was blood cortisol (n = 13). None of the included studies were assessed as having a low risk of bias, mostly due to a lack of reporting blinding procedures and reasons for missing data. Using a three-level random effect model, we concluded that multimodal analgesia reduced blood cortisol concentrations in the first hour following surgical castration in comparison to the control group; this effect was diminished but still evident at 3 and 4 h, but not beyond at 6, 12 and 24 h. Too few data were available to meaningfully assess other outcomes and methods. Variability in methods and outcomes between studies, and risks of bias, hinder our capacity to provide science-based recommendations for best practice.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".