Chronic Postoperative Opioid Use: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.001 | 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) | 0.000 | 0.001 |
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 it