Clustering of opioid prescribing and opioid-related mortality among family physicians in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To examine whether variation in prescribing at the level of the individual physician is associated with opioid-related mortality. DESIGN: A population-based cross-sectional analysis linking prescription data with records from the Office of the Chief Coroner. SETTING: The province of Ontario. Participants Family physicians in Ontario and Ontarians aged 15 to 64 who were eligible for prescription drug coverage under the Ontario Public Drug Program. MAIN OUTCOME MEASURES: Variation in family physicians' opioid prescribing and opioid-related mortality among their patients. RESULTS: The 20% of family physicians (n = 1978) who prescribed opioids most frequently issued opioid prescriptions 55 times more often than the 20% who prescribed opioids least frequently. Family physicians in the uppermost quintile also wrote the final opioid prescription before death for 62.7% of public drug plan beneficiaries whose deaths were related to opioids. Physician characteristics associated with greater opioid prescribing were male sex (P = .003), older age (P < .001), and a greater number of years in practice (P < .001). CONCLUSION: Opioid prescribing varies remarkably among family physicians, and opioid-related deaths are concentrated among patients treated by physicians who prescribe opioids frequently. Strategies to reduce opioid-related harm should include efforts focusing on family physicians who prescribe opioids frequently.
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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 | 0.000 | 0.000 |
| 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".