Arthroscopy Association of Canada Position Statement on Opioid Prescription After Arthroscopic Surgery
Bibliographic record
Abstract
Background: Despite the ongoing opioid epidemic, most patients are still prescribed a significant number of opioid medications for pain management after arthroscopic surgery. There is a need for consensus among orthopaedic surgeons and solutions to aid providers in analgesic strategies that reduce the use of opioid pain medications. Purpose: This position statement was developed with a comprehensive systematic review and meta-analysis of exclusively randomized controlled trials (RCTs) to synthesize the best available evidence for managing acute postoperative pain after arthroscopic surgery. Study Design: Position statement. Methods: , and associated variations. We included exclusively RCTs on adult patients to gather the best available evidence for managing acute postoperative pain after arthroscopic surgery. Patient characteristics, pain, and opioid data were extracted, data were analyzed, and trial bias was evaluated. Results: A total of 21 RCTs were identified related to the prescription of opioid-sparing pain medication after arthroscopic surgery. The following recommendations regarding noninvasive, postoperative pain management strategies were made: (1) multimodal oral nonopioid analgesic regimens-including at least 1 of acetaminophen-a nonsteroidal anti-inflammatory drug-can significantly reduce opioid consumption with no change in pain scores; (2) cryotherapy is likely to help with pain management, although the evidence on the optimal method of application (continuous-flow vs ice pack application) is unclear; (3) and (4) limited RCT evidence supports the efficacy of transcutaneous electrical nerve stimulation and relaxation exercises in reducing opioid consumption after arthroscopy; and (5) limited RCT evidence exists against the efficacy of transdermal lidocaine patches in reducing opioid consumption. Conclusion: A range of nonopioid strategies exist that can reduce postarthroscopic procedural opioid consumption with equivalent vocal pain outcomes. Optimal strategies include multimodal analgesia with education and restricted/reduced opioid prescription.
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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 | 0.044 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".