Effect of perioperative multimodal analgesia protocol on pain scores and recovery in dogs undergoing cruciate ligament repair
Bibliographic record
Abstract
Imagine a 30-kg Labrador Retriever recovering from tibial plateau levelling osteotomy (TPLO): despite receiving a standard opioid protocol, the dog is vocalising, reluctant to bear weight, and showing elevated heart rate six hours post-surgery. This scenario, familiar to veterinary surgeons worldwide, highlights the limitations of single-agent analgesia for orthopaedic procedures [1]. This research compared a multimodal analgesia protocol combining methadone, fentanyl constant rate infusion (CRI), intra-articular bupivacaine, meloxicam, and gabapentin against a standard methadone-meloxicam protocol in 32 dogs undergoing TPLO at the National University of San Marcos veterinary teaching hospital, Lima, Peru. Pain was assessed using the Glasgow Composite Measure Pain Scale-Short Form (CMPS-SF) at seven time points over 48 hours. The multimodal group showed significantly lower pain scores at all post-operative time points (p < 0.01), with the greatest difference at 2 hours (3.8 ± 1.2 vs 5.6 ± 1.4, p < 0.001). Time to first unassisted weight-bearing was 40% shorter in the multimodal group (8.4 ± 2.1 vs 14.2 ± 3.6 hours, p < 0.001). Rescue analgesia was required in 12.5% of multimodal vs 43.8% of standard cases. These findings support adoption of multimodal protocols for TPLO in dogs.
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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) | 0.001 | 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".