Single-dose premedication enhances multimodal analgesia after knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: With the current trend to reduce postoperative opioid use to enhance recovery and address perioperative opioid addiction concerns, the challenge of managing pain after total knee arthroplasty has increased. This study examined the effect of adding a preoperative medication regime to a multimodal postoperative analgesia protocol that included regional anaesthesia. MATERIALS AND METHODS: Sixty patients undergoing elective first-time unilateral knee arthroplasty received celecoxib 100mg, gabapentin 600mg and dexamethasone 10mg po one hour before skin incision. They were compared to a sequential retrospective cohort of 49 patients. All patients routinely received acetaminophen 650mg po q6h, ibuprofen 400mg po q8h, patient-controlled opioid analgesia and continuous adductor canal blocks postoperatively. Pain scores and opioid consumption were recorded at 4, 8, 12, 24 and 48h. RESULTS: Pain scores and cumulative opioid use were statistically and clinically significantly reduced at all time points up to 48h. CONCLUSIONS: Combining preoperative oral celecoxib, gabapentin and dexamethasone had a clinically significantly effect in reducing pain scores and opioid use for at least 48h. Most of this effect is probably due to dexamethasone.
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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".