Opioid-Sparing Strategies in Arthroscopic Surgery
Bibliographic record
Abstract
BACKGROUND: Opioid overprescription is a problem in orthopaedic surgery. Arthroscopic surgery, given its minimally invasive nature, represents an opportunity to minimize opioid prescription and consumption by using effective pain management adjuncts. Thus, the primary question posed in this study was which noninvasive pain management modalities can effectively manage pain and reduce opioid intake after arthroscopic surgery. METHODS: The databases PubMed, MEDLINE, EMBASE, Scopus, and Web of Science were searched on August 10, 2022. Randomized controlled trials (RCTs) evaluating noninvasive pain management strategies in arthroscopy patients were evaluated. Eligible studies were selected through a systematic screening process. Meta-analysis was performed for pain scores and opioid consumption at time points which had sufficient data available. RESULTS: Twenty-one RCTs were included, with a total of 2,148 patients undergoing shoulder, knee, and hip arthroscopy. Meta-analysis comparing nonopioid, oral analgesic regimens, with or without patient education components, with the standard of care or placebo demonstrated no difference in pain scores at 24 hours, 4 to 7 days, or 14 days postoperatively. Nonopioid regimens also resulted in significantly lower opioid consumption in the first 24 hours postoperatively (mean difference, -37.02 mg oral morphine equivalents, 95% confidence interval, -74.01 to -0.03). Transcutaneous electrical nerve stimulation (TENS), cryotherapy, and zolpidem were also found to effectively manage pain and reduce opioid use in a limited number of studies. CONCLUSIONS: A range of noninvasive pain management strategies exist to manage pain and reduce opioid use after arthroscopic procedures. The strongest evidence base supports the use of multimodal nonopioid oral analgesics, with some studies incorporating patient education components. Some evidence supports the efficacy of TENS, cryotherapy, and nonbenzodiazepine sleeping aids. Direction from governing bodies is an important next step to incorporate these adjuncts into routine clinical practice to manage pain and reduce the amount of opioids prescribed and consumed after arthroscopic surgery. LEVEL OF EVIDENCE: Level II, systematic review and meta-analysis of RCTs. See Instructions for Authors for a complete description of the levels of evidence.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".