Proportional Hazards Frailty Models for Recurrent Methadone Maintenance Treatment
Bibliographic record
Abstract
The authors' objective in this study was to identify determinants of time to discontinuation of methadone maintenance treatment (MMT) across multiple treatment episodes. Population-level data on drug dispensations for all patients receiving methadone for opioid maintenance throughout the tenure of the British Columbia, Canada, methadone program to date (1996-2007) were extracted from an administrative database. Proportional hazards frailty models were developed to assess factors associated with time to discontinuation from recurrent MMT episodes. A total of 17,005 patients experienced 32,656 treatment episodes over the 11-year follow-up period. Age, medical comorbidity, and physician patient load, as well as neighborhood-level socioeconomic status indicators, were significant predictors of time to discontinuation of treatment; treatment adherence and average daily doses up to and above 120 mg per day were also associated with longer treatment episodes. Studies have shown that while successfully retained in MMT, clients decrease their illicit drug use and criminal activity, and their risk of mortality is substantially lower; however, the majority of clients relapse. Many reenter treatment. The primary finding of this study was that patients experiencing multiple treatment episodes tended to stay in treatment for progressively longer periods in later episodes.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".