A randomised controlled trial investigating the effects of administering a non‐steroidal anti‐inflammatory drug to beef calves assisted at birth and risk factors associated with passive immunity, health, and growth
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to investigate the impact of pain mitigation at birth to assisted beef calves and determine the risk factors associated with transfer of passive immunity (TPI), health, and growth. METHODS: Two hundred and thirty cow-calf pairs requiring calving assistance were enrolled. Calves were randomised to receive meloxicam (0.5 mg/kg) or an equivalent volume of placebo subcutaneously at birth. Calf blood samples were collected between one and seven days of age to determine serum immunoglobulin (IgG) concentration. Colostrum intake, treatment for disease, mortality, and weaning weights were recorded. Multilevel linear or logistic regression models were used to determine the effects of meloxicam and to identify risk factors. RESULTS: There was no effect of meloxicam on serum IgG concentrations, average daily gain (ADG), or risk of inadequate TPI (serum IgG concentration <24 g/l), treatment for disease, or mortality (P>0.05). Bottle or tube feeding calves were associated with decreased serum IgG concentrations (P=0.01) compared with nursing. Calves with an incomplete tongue withdrawal reflex had higher odds of being treated for disease compared with those with complete withdrawal (P=0.009). Being born meconium-stained and having decreased serum IgG concentrations were associated with an increased risk of mortality (P=0.03). Being born of a mature cow, having a higher birth weight, and increased serum IgG concentrations were associated with greater ADG to weaning (P<0.05). CONCLUSION: Vigour assessment at birth along with good colostrum management may be important to improve TPI and health in high-risk calves such as those assisted at birth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".