Incidence of persistent postoperative opioid use in patients undergoing ambulatory surgery: a retrospective cohort study
Bibliographic record
Abstract
The opioid crisis remains a major public health concern. In ambulatory surgery, persistent postoperative opioid use is poorly described and temporal trends are unknown. A population-based retrospective cohort study was undertaken in Ontario, Canada using routinely collected administrative data for adults undergoing ambulatory surgery between 1 January 2013 and 31 December 2017. The primary outcome was persistent postoperative opioid use, defined using best-practice methods. Multivariable generalised linear models were used to estimate the association of persistent postoperative opioid use with prognostic factors. Temporal trends in opioid use were examined using monthly time series, adjusting for patient-, surgical- and hospital-level variables. Of 340,013 patients, 44,224 (13.0%, 95%CI 12.9-13.1%) developed persistent postoperative opioid use after surgery. Following multivariable adjustment, the strongest predictors of persistent postoperative opioid use were pre-operative: utilisation of opioids (OR 9.51, 95%CI 8.69-10.39); opioid tolerance (OR 88.22, 95%CI 77.21-100.79); and utilisation of benzodiazepines (OR 13.75, 95%CI 12.89-14.86). The time series model demonstrated a small but significant trend towards decreasing persistent postoperative opioid use over time (adjusted percentage change per year -0.51%, 95%CI -0.83 to -0.19%, p = 0.003). More than 10% of patients who underwent ambulatory surgery experienced persistent postoperative opioid use; however, there was a temporal trend towards a reduction in persistent opioid use after surgery. Future studies are needed that focus on interventions which reduce 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".