The effects of a provincial opioid prescribing standard on opioid prescribing for pain: interrupted series analysis
Bibliographic record
Abstract
ABSTRACT Background In 2016, the College of Physicians and Surgeons of British Columbia released a legally enforceable opioid prescribing practice standard for the treatment of chronic non-cancer pain (CNCP). The standard was revised in 2018, following physicians, patient groups and key partners’ concerns it was inappropriately interpreted. We tested the effects of the practice standard on access to opioids for people living with CNCP; and spillover effects on people living with cancer or receiving palliative care. Methods We used comprehensive administrative health data and multiple baseline interrupted time series analysis to evaluate the effects of the 2016 practice standard and 2018 revision. Results The practice standard accelerated pre-existing declining trends in morphine milligram equivalents (MME) dispensed per person living with CNCP (−0.1%, 95% CI: -0.2, 0.0%), but also for people living with cancer (−0.7%, 95% CI: -1.0, -0.5%) or receiving palliative care (−0.3%, 95% CI: -0.5, 0.0%). Trends for the proportion of people with CNCP prescribed an opioid >90 MME daily dose (−0.3%, 95% CI: -0.4, 0.2%), co-prescribed benzodiazepine or other hypnotic (−0.6%, 95% CI: -0.7, -0.5%), and rapidly tapered (0.1%, 95% CI: -0.2, 0.0%) also declined more quickly. While level effects were generally in the same direction, the proportion of people rapidly tapered immediately post-implementation increased 2.0% (95% CI: 0.4, 3.3%). Trends slowed or reversed post-2018 revision. Interpretation The 2016 practice standard was associated with an immediate and long-lasting effect on physicians’ opioid prescribing behaviours, including negative spillover effects on tapering, and for people living with cancer or receiving palliative care.
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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 | 0.029 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".