Changing patterns of opioid initiation for pain management in Ontario, Canada: A population-based cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: The recent publication of a national guideline and quality standards in Canada have provided clinicians with new, evidence-based recommendations on safe, appropriate opioid use. We sought to characterize how well opioid initiation practices aligned with these recommendations before and following their release. METHODS: We conducted a population-based study among people initiating opioids prior to the release of national guidelines (April 2015-March 2016; fiscal year [FY] 2015) and in the most recent year available (January-December 2019) in Ontario, Canada. We used linked administrative claims data to ascertain the apparent indication for opioid therapy, and characterized the initial daily dose (milligrams morphine or equivalent; MME) and prescription duration for each indication. RESULTS: In FY2015, 653,885 individuals commenced opioids, compared to 571,652 in 2019. Over time, there were small overall reductions in the prevalence of initial daily doses exceeding 50MME (23.9% vs. 20.1%) and durations exceeding 7 days (17.4% vs. 14.8%); but the magnitude of the reductions varied widely by indication. The prevalence of high dose (>50MME) initial prescriptions reduced significantly across all indications, with the exception of dentist-prescribed opioids (13.6% vs. 12.1% above 50MME). In contrast, there was little change in initial durations exceeding 7 days across most indications, with the exception of some surgical indications (e.g. common excision; 9.3% vs. 6.2%) and among those in palliative care (35.2% vs. 29.2%). CONCLUSION: Despite some modest reductions in initiation of high dose and long duration prescription opioids between 2015 and 2019, clinical practice is highly variable, with opioid prescribing practices influenced by clinical indication. These findings may help identify medical specialties well-suited to targeted interventions to promote safer opioid prescribing.
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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 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".