Clinical indications associated with opioid initiation for pain management in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
Concerns over prescription opioids contributing to high levels of opioid use disorder and overdose have led policymakers and clinicians to seek means to reduce inappropriate and high-dose initial prescriptions. To inform such efforts, we sought to describe the clinical indications associated with opioid initiation and the characteristics of the initial prescriptions and patients through a retrospective population-based cohort study. Our cohort included Ontarians initiating prescription opioids for pain management between April 1, 2015, and March 31, 2016. We identified the apparent clinical indication for opioid initiation by linking prescription drug claims to procedural and diagnostic information on health service records on the day of, and 5 days preceding prescription. Outcomes included initial opioid type, prescription duration, and daily dose (in milligram morphine equivalents), stratified either by indication or indication cluster. Among 653,993 individuals, we successfully classified 575,512 (88.0%) people initiating opioids into 23 clinical indications in 6 clusters: dental (23.2%); postsurgical (17.4%); musculoskeletal (12.0%); trauma (11.2%); cancer/palliative care (6.5%); and other less frequent indications (17.7%). Individuals with postsurgical pain received the highest daily doses (40.5% with greater than 50 milligram morphine equivalent), and those with musculoskeletal pain received more initial prescriptions with a duration exceeding 7 days (34.2%). Opioids are initiated for a wide range of indications with varying doses and durations; yet, those who initiated opioids for postsurgical and musculoskeletal pain received the greatest doses and durations of therapy, respectively. These findings may help tailor and prioritize efforts to promote more appropriate opioid prescribing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".