The behavioral assessment and alleviation of pain associated with castration in beef calves treated with flunixin meglumine and caudal lidocaine epidural anesthesia with epinephrine.
Bibliographic record
Abstract
The objectives of this study were 1) to determine the effects of flunixin megulmine in combination with caudal epidural anesthesia as a postoperative analgesic in beef calves following surgical castration, and 2) to consider stride length and pedometry as potential behavioral assessment tools for detecting postcastration pain. Surgical castration was performed in 101 beef calves randomly assigned to 3 treatment subgroups: 1) castration without anesthesia (SURG); 2) castration following lidocaine with epinephrine caudal epidural anesthesia (SURG + EPI); 3) castration following lidocaine with epinephrine caudal epidural anesthesia and flunixin meglumine (SURG + EPI + F). Several outcomes, including pedometer counts, changes in stride length, subjective visual assessment of pain, instantaneous scan sampling of the calves' postoperative activities, and the amount of movement and vocalization during the castration procedure, were measured to identify and quantify pain. The results indicated that stride length and the number of steps taken by calves after castration appear to be good measures of pain. Significant differences found between treatment groups for stride length and visual assessments suggest that flunixin meglumine can be considered to provide visible pain relief up to 8 hours postcastration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".