Reduction of opioid use after orthopedic surgery: a scoping review
Bibliographic record
Abstract
BACKGROUND: The opioid epidemic is one of the biggest public health crises of our time, and overprescribing of opioids after surgery has the potential to lead to long-term use. The purpose of this review was to identify and summarize the available evidence on interventions aimed at reducing opioid use after orthopedic surgery. METHODS: We searched CENTRAL, Embase and Medline from inception until August 2019 for studies comparing interventions aimed at reducing opioid use after orthopedic surgery to a control group. We recorded demographic data and data on intervention success, and recorded or calculated percent opioid reduction compared to control. RESULTS: We included 141 studies (20 963 patients) in the review, of which 113 (80.1%) were randomized controlled trials (RCTs), 6 (4.3%) were prospective cohort studies, 16 (11.4%) were retrospective cohort studies, 5 (3.6%) were case reports, and 1 (0.7%) was a case series. The majority of studies (95 [67.4%]) had a follow-up duration of 2 days or less. Interventions included the use of local anesthetics and/or nerve blocks (42 studies [29.8%]), nonsteroidal anti-inflammatory drugs (31 [22.0%]), neuropathic pain medications (9 [6.4%]) and multimodal analgesic combinations (25 [17.7%]. In 127 studies (90.1%), a significant decrease in postoperative opioid consumption compared to the control intervention was reported; the median opioid reduction in these studies was 39.7% (range 5%-100%). Despite these reductions in opioid use, the effect on pain scores and on incidence of adverse effects was inconsistent. CONCLUSION: There is a large body of evidence from randomized trials showing the promise of a variety of interventions for reducing opioid use after orthopedic surgery. Rigorously designed RCTs are needed to determine the ideal interventions or combination of interventions for reducing opioid use, for the good of patients, medicine and society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".