Changes in opioid distribution and chronic opioid users following outpatient orthopedic surgery: a pre–post intervention study
Bibliographic record
Abstract
BACKGROUND: Outpatient overprescribing of opioids in the postoperative period contributes to the opioid epidemic. Given that patient education and evidence-informed prescription protocols have reduced postoperative opioid use in small, randomized trials, we sought to evaluate the effectiveness of a multimodal opioid reduction protocol, implemented institution-wide at an outpatient Canadian orthopedic surgery centre. METHODS: In this pre-post intervention study, we used deidentified health administrative data from a provincial data repository to identify all opioid-naive patients who underwent outpatient shoulder or knee surgery at a single institution between 2013 and 2022. An opioid restriction protocol was implemented in 2019, including an educational pamphlet, perioperative verbal education, and a standardized postoperative analgesic prescription. Outcomes analyzed included dispensed morphine milligram equivalents (MME) per patient within 180 days of surgery and chronic opioid use, defined as opioids dispensed 180-270 days after surgery. Prescriptions dispensed from any provider were included. RESULTS: < 0.001). These findings remained consistent after adjustment for age, sex, socioeconomic status, mental health, and medical comorbidity in multivariable regression analyses. CONCLUSION: The volume of opioids dispensed and the number of chronic opioid users were significantly reduced among patients who underwent outpatient orthopedic surgery after the institution-wide implementation of a multimodal postoperative opioid reduction protocol.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".