A regional approach to reduce postoperative opioid prescribing in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Opioid-related morbidity and mortality continue to rise in the province of Ontario. We implemented a provincial campaign to reduce the number of opioid pills prescribed at discharge after surgery in the Ontario Surgical Quality Improvement Network (ON-SQIN). METHODS: Activities related to the provincial campaign were implemented between April 2019 and March 2020 and between October 2020 and March 2021. Self-reported data from participating hospitals were used to determine changes in postoperative opioid prescribing patterns across participating hospitals. RESULTS: A total of 33 and 26 hospitals participated in the provincial campaign in the first and second year, respectively. During the first year of the campaign, the median morphine equivalent (MEQ) from opioid prescriptions decreased significantly in a number of surgical specialties, including General Surgery (from 105 [75-130] to 75 [55-107], P < 0.001) (median, interquartile range) and Orthopedic Surgery (from 450 [239-600] to 334 [167-435], P < 0.001). The median number of opioid pills prescribed at discharge per surgery also decreased significantly, from 25 (15-53) to 15 (11-38) for 1 mg hydromorphone (P < 0.001) and 25 (20-51) to 20 (15-30) for oxycodone (P < 0.001). The decrease in opioid prescriptions continued in the second year of the campaign. CONCLUSIONS: Our approach resulted in a significant reduction in the number of postoperative opioids prescribed across a number of surgical specialties. Our findings indicate that evidence-based strategies derived from a regional collaborative network can be leveraged to promote and sustain quality improvement activities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".