Perioperative nonopioid analgesia reduces postoperative opioid consumption in knee arthroscopy: a systematic review and meta‐analysis
Bibliographic record
Abstract
PURPOSE: The opioid epidemic has prompted an emphasis on investigating opioid-sparing alternatives for pain management following knee arthroscopy. This review evaluated the effects of perioperative nonopioid adjunct analgesia on postoperative opioid consumption and pain control in patients undergoing knee arthroscopy. METHODS: A systematic review and meta-analysis was performed using the following databases: PubMed, Embase, Web of Science, MEDLINE, and SCOPUS. Prospective comparative studies assessing the efficacy of various perioperative nonopioid analgesic strategies in patients undergoing knee arthroscopy were included. Twenty-five studies (n = 2408) were included. RESULTS: Pre-emptive nonopioid pain medications demonstrated a reduction in cumulative postoperative oral morphine equivalent (OME) consumption by 11.8 mg (95% CI - 18.3, - 5.4, p ≤ 0.0001) and VAS pain scores by 1.5 (95% CI - 2.3, - 0.7, p < 0.001) at 24 h compared to placebo. Postoperative nonopioid pain medications significantly reduced cumulative postoperative OME consumption by 9.7 mg (95% CI - 14.4, - 5.1, p < 0.001) and VAS pain scores by 1.0 (95% CI - 1.354, - 0.633, p < 0.001) at 24 h compared to placebo. Saphenous nerve blocks significantly reduced cumulative postoperative OME consumption by 6.5 mg (95% CI - 10.3, - 2.6, p = 0.01) and VAS pain scores by 0.8 (- 1.4, - 0.3, p = 0.03) at 24 h compared to placebo. Both preoperative patient education and postoperative cryotherapy reduced postoperative opioid consumption. CONCLUSION: Perioperative nonopioid pharmacotherapy, saphenous nerve blocks, and cryotherapy for patients undergoing knee arthroscopy significantly reduce opioid consumption and pain scores when compared to placebo at 24 h postoperatively. These interventions should be considered in efforts to reduce opioid consumption in patients undergoing knee arthroscopy. More research is needed to determine which interventions can reduce pain outside of the immediate postoperative period and the potential synergistic effects of combining interventions. LEVEL OF EVIDENCE: II.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".