Predictors of treatment retention in a major methadone maintenance treatment program in iran: a survival analysis.
Bibliographic record
Abstract
BACKGROUND: To identify correlates related to retention time of a cohort study of the opioid-dependent patients participating in the Methadone Maintenance Treatment (MMT) program offered by a major addiction treatment clinic in Tehran, Iran between April 2007 and March 2011. METHODS: Several parametric Survival models assuming Weibull, Log-normal and Log-logistic distributions were compared to search for association between covariates and risk of relapse and dropping out of treatment among 198 patient participants. RESULTS: According to Akaike Information Criterion (AIC), Log-normal model had the best fitting. Estimates of this model indicated that increase in average methadone dosage was associated with longer retention time. Correlates associated with shorter retention time were suffering from mental disorders, using stimulant drugs, being poly-substance dependents and having prior treatments. CONCLUSIONS: Findings of this study provide support for giving more attention to patients who are poly-substance or stimulant-drug dependents, have non-substance psychiatric comorbidity and the ones with addiction treatment history. Independent of patient characteristics, retention improved as the dose of methadone increased.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".