Chronic Postoperative Opioid Use: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: There are a number of studies in the literature that describe the prevalence, causes, and factors associated with chronic postoperative opioid use, but there is a lack of synthesis of the literature to guide clinicians in optimally managing postoperative pain while avoiding opioid dependence. Thus, the goal of this study was to perform a systematic review of the literature to investigate the prevalence of chronic postoperative opioid use and the associated risk factors. MATERIALS AND METHODS: A systematic search was performed using Ovid Medline and Embase according to PRISMA guidelines. Data were collected on the following outcomes of interest: prevalence of opioid use at 3, 6, and 12 months postoperatively, and risk factors associated with chronic postoperative opioid use. RESULTS: Forty-three articles were included in the final analysis. The mean prevalence of chronic postoperative opioid use in all populations at 3, 6, and 12 months postoperatively was 30.5%, 25.6%, and 25.2%, respectively. The prevalence of patients who developed chronic opioid use at 3, 6, and 12 months postoperatively was 10.4%, 8.5%, and 9.8%, respectively. Forty of the articles analyzed risk factors associated with chronic postoperative opioid use. The most common associated risk factor identified was preoperative opioid use with 27 articles demonstrating a significant association with chronic postoperative opioid use. DISCUSSION: The current opioid crisis is in part secondary to the prevalence of chronic opioid use following surgery. This study identified associated risk factors with chronic postoperative opioid use, which may help identify patients at risk for developing chronic 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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".