Opioid use after outpatient elective general surgery: quantifying the burden of persistent use
Bibliographic record
Abstract
Purpose: Surgery is a major risk factor for chronic opioid use among patients who had not recently been prescribed opioids. This study identifies the rate of, and risk factors for, persistent opioid use following laparoscopic cholecystectomy and open inguinal hernia repair in patients not recently prescribed opioids. Methods: This retrospective population-based cohort study included all patients who had not been prescribed opioids in the 6 months prior to undergoing open inguinal hernia repair or laparoscopic cholecystectomy from January 2013 to July 2016 in Ontario. Opioid prescription was identified from the provincial Narcotics Monitoring System and data were obtained from the Institute for Clinical Evaluative Sciences. The primary outcome was persistent opioid use after surgery (3, 6, 9 and 12 months). Associated risk factors and prescribing patterns were also examined. Results: Among the 90,326 patients in the study cohort, 80% filled an opioid prescription after surgery, with 11%, 9%, 5% and 1% filling a prescription at 3, 6, 9 and 12 months, respectively. Significant variability was identified in the type of opioid prescribed (41% codeine, 31% oxycodone, 18% tramadol) and in regional prescribing patterns (mean prescription/region range, 135-225 oral morphine equivalents). Predictors of continued opioid use included age, female gender, lower income quintile and being operated on by less experienced surgeons. Conclusion: Most patients who undergo elective cholecystectomy and hernia repair will fill a prescription for an opioid after surgery, and many will continue to fill opioid prescriptions for considerably longer than clinically anticipated. There is important variability in opioid type, regional prescribing patterns and risk factors that identify strategic targets to reduce the opioid burden in this patient population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".