American Society for Enhanced Recovery and Perioperative Quality Initiative-4 Joint Consensus Statement on Persistent Postoperative Opioid Use: Definition, Incidence, Risk Factors, and Health Care System Initiatives
Bibliographic record
Abstract
Persistent postoperative opioid use is thought to contribute to the ongoing opioid epidemic in the United States. However, efforts to study and address the issue have been stymied by the lack of a standard definition, which has also hampered efforts to measure the incidence of and risk factors for persistent postoperative opioid use. The objective of this systematic review is to (1) determine a clinically relevant definition of persistent postoperative opioid use, and (2) characterize its incidence and risk factors for several common surgeries. Our approach leveraged a group of international experts from the Perioperative Quality Initiative-4, a consensus-building conference that included representation from anesthesiology, surgery, and nursing. A search of the medical literature yielded 46 articles addressing persistent postoperative opioid use in adults after arthroplasty, abdominopelvic surgery, spine surgery, thoracic surgery, mastectomy, and thoracic surgery. In opioid-naïve patients, the overall incidence ranged from 2% to 6% based on moderate-level evidence. However, patients who use opioids preoperatively had an incidence of >30%. Preoperative opioid use, depression, factors associated with the diagnosis of substance use disorder, preoperative pain, and tobacco use were reported risk factors. In addition, while anxiety, sex, and psychotropic prescription are associated with persistent postoperative opioid use, these reports are based on lower level evidence. While few articles addressed the health policy or prescriber characteristics that influence persistent postoperative opioid use, efforts to modify prescriber behaviors and health system characteristics are likely to have success in reducing persistent postoperative opioid use.
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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 | 0.137 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".