Six-Year Outcome of Opioid Maintenance Treatment in Heroin-Dependent Patients: Results from a Naturalistic Study in a Nationally Representative Sample
Bibliographic record
Abstract
BACKGROUND: In many countries, the opioid agonists, buprenorphine and methadone, are licensed for maintenance treatment of opioid dependence. Many short-term studies have been performed, but little is known about long-term effects. Therefore, this study described over 6 years (1) mortality, retention and abstinence rates and (2) changes in concomitant drug use and somatic and mental health. METHODS: A prevalence sample of n = 2,694 maintenance patients, recruited from a nationally representative sample of n = 223 substitution doctors, was evaluated in a 6-year prospective-longitudinal naturalistic study. At 72 months, n = 1,624 patients were assessed for outcome; 1,147 had full outcome data, 346 primary outcome data and 131 had died; 660 individuals were lost to follow-up. RESULTS: The 6-year retention rate was 76.6%; the average mortality rate was 1.1%. During follow-up, 9.4% of patients became "abstinent" and 1.9% were referred for drug-free addiction treatment. Concomitant drug use decreased and somatic health status and social parameters improved. CONCLUSIONS: The study provides further evidence for the efficacy and safety of maintenance treatment with opioid agonists. In the long term, the number of opioid-free patients is low and most patients are more or less continuously under opioid maintenance therapy. Further implications are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".