Defining dosing pattern characteristics of successful tapers following methadone maintenance treatment: results from a population‐based retrospective cohort study
Bibliographic record
Abstract
AIMS: To identify dose-tapering strategies associated with sustained success following methadone maintenance treatment (MMT). DESIGN: Population-based retrospective cohort study. SETTING: Linked administrative medication dispensation data from British Columbia, Canada. PARTICIPANTS: From 25 545 completed MMT episodes, 14 602 of which initiated a taper, 4183 individuals (accounting for 4917 MMT episodes) from 1996 to 2006 met study inclusion criteria. MEASUREMENTS: The primary outcome was sustained successful taper, defined as a daily dose ≤5 mg per day in the final week of the treatment episode and no treatment re-entry, opioid-related hospitalization or mortality within 18 months following episode completion. FINDINGS: The overall rate of sustained success was 13% among episodes meeting inclusion criteria (646 of 4917), 4.4% (646 of 14 602) among all episodes initiating a taper and 2.5% (646 of 25 545) among all completed episodes in the data set. The results of our multivariate logistic regression analyses suggested that longer tapers had substantially higher odds of success [12-52 weeks versus <12 weeks: odds ratio (OR): 3.58; 95% confidence interval (CI): 2.76-4.65; >52 weeks versus <12 weeks: OR: 6.68; 95% CI: 5.13-8.70], regardless of how early in the treatment episode the taper was initiated, and a more gradual, stepped tapering schedule, with dose decreases scheduled in only 25-50% of the weeks of the taper, provided the highest odds of sustained success (versus <25%: OR: 1.61; 95% CI: 1.22-2.14). CONCLUSIONS: The majority of patients attempting to taper from methadone maintenance treatment will not succeed. Success is enhanced by gradual dose reductions interspersed with periods of stabilization. These results can inform the development of a more refined guideline for future clinical practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".