Flunixin Meglumine Is Superior to Meloxicam for Providing Analgesia after Surgical Castration in 2-Month-Old Goats
Bibliographic record
Abstract
Farm animals are exposed to various painful procedures during their productive lives, making it necessary to implement anesthetic and analgesic protocols. However, there are few studies evaluating the effectiveness of these drugs. Our objective was to compare the analgesic effects of two nonsteroidal anti-inflammatory drugs (NSAIDs): meloxicam (MEL) and flunixin meglumine (FLU), in goat kids subjected to surgical castration under local anesthesia. Anglo-Nubian goat kids (60 days old) were allocated into two groups: MEL (n = 9), and FLU (n = 8), each administered 5 min before starting castration. All had been previously subjected to local anesthesia with lidocaine, injected bilaterally into the testes, plus subcutaneous in the scrotal raphe. Pain sensitivity was evaluated using the von Frey monofilaments test. Reactions were recorded before castration (M0), immediately after castration (M1), and once-daily for three consecutive days post-castration (M2, M3, and M4, respectively). Pain assessments were conducted in three body regions: at four points of the scrotum (dorsal and ventral; left and right lateral; R1); medial region of the pelvic limb, gracilis muscle (R2); and hypogastric region of the abdomen (R3). MEL goats had considerably greater pain reaction in R1 and R2 over time, mainly in M2; therefore, FLU was a more effective analgesic than MEL, resulting in less pain reaction.
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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 | 0.000 |
| 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) | 0.001 | 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".