Field study to evaluate the effects of meloxicam NSAID therapy and calving assistance on newborn calf vigor, improvement of health and growth in pre-weaned Holstein calves
Bibliographic record
Abstract
The objective of this research was to evaluate the usefulness of a novel calf VIGOR assessment tool to measure birth trauma and associations with future health and growth. In addition, pain management therapy using meloxicam injectable solution was evaluated for calves suffering from birth trauma and reduced vigor. A total of 842 heifer and bull calves from 10 commercial dairy herds were enrolled in a randomized, double-blind clinical field trial. At birth, newborn VIGOR was evaluated by the dairy producer to assess the Visual appearance, Initiation of movement, General responsiveness, Oxygenation, as well as heart and respiration Rate of the calf. Subsequently, calves were administered either a 1.0 mL subcutaneous injection of meloxicam or placebo solution. Each calf was measured for growth and assessed using a standardized clinical score for health at 1, 2, 3, and 6 weeks of age. Compared to unassisted calvings, calves born with assistance had lower vigor. Assisted calves treated with meloxicam had improved weight gain in the first week compared to placebo-treated calves. In contrast, treatment with meloxicam resulted in lower gains in observed but unassisted calves. Calves with improved newborn vigor and better health had significantly greater weight gain up to 6 weeks of age. Meloxicam-treated calves had better health from birth to 6 weeks of age. Overall, the calf VIGOR score is a good indicator of trauma at calving. Meloxicam therapy shows promise for improving health and growth, particularly for calves born with assistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".