Use of other opioids during methadone therapy: a population‐based study
Bibliographic record
Abstract
AIMS: To determine the extent to which other opioids are prescribed to patients receiving methadone in Ontario, Canada. DESIGN: Retrospective cohort study. SETTING: Ontario, Canada from 1 April 2003 to 31 March 2010. PARTICIPANTS: We studied patients aged 15-64 years with publically funded drug coverage who received at least 30 days of continuous methadone maintenance therapy (MMT). MEASUREMENTS: The proportion of patients who received more than 7 days of a non-methadone opioid during MMT. A secondary analysis examined the extent to which non-methadone opioids were prescribed by physicians or dispensed by pharmacies not involved in a patient's MMT. FINDINGS: Among 18,759 patients treated with methadone, 3456 (18.4%) received at least one prescription for non-methadone opioids of more than 7 days' duration. In this group, the median number of non-methadone opioid prescriptions dispensed per year was 11.9 (interquartile range 4.1-25.0). The most frequently prescribed opioids were codeine and oxycodone. Of the 73,520 non-methadone opioid prescriptions of more than 7 days' duration, nearly half (45.8%) originated from non-MMT prescribers and pharmacies. CONCLUSIONS: Many patients receiving methadone maintenance therapy in Ontario receive overlapping prescriptions for other opioids, often for extended periods. The associated prescribing patterns suggest that many such prescriptions may be duplicitous. The prescribing and dispensing of non-methadone opioids to patients receiving methadone maintenance therapy is likely to be observed in jurisdictions outside Ontario, Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".