Criminal Behavior in Opioid‐Dependent Patients Before and During Maintenance Therapy: 6‐year Follow‐Up of a Nationally Representative Cohort Sample
Bibliographic record
Abstract
Lifetime prevalence of opioid dependence is about 0.4% in western countries. Opioid-dependent patients have high morbidity and mortality and a high risk of criminal behavior. Few studies have addressed the long-term impact of opioid maintenance therapy on convictions and criminal behavior. The PREMOS study is a prospective, longitudinal, naturalistic clinical study of a nationally representative sample of 2694 opioid-dependent patients to investigate convictions and criminal behavior at baseline and after 6 years of maintenance treatment. At follow-up, 2284 patients still were eligible (84.7%). A comprehensive assessment including a patient and doctor questionnaire, and the EuropASI was completed at baseline and follow-up. Data on criminality at follow-up had been received for 1147 (70.6%) patients. A large number (84.5%) of them had been charged or convicted at any time before baseline assessment, most frequently with drug-related offenses (66.8%), acquisitive crime (49.1%), or acts of violence (22.0%). Reported charges and convictions had declined to 17.9% for the last 12 months before follow-up, which was also reflected by a significant decrease in the EuropASI subscore "legal problems" from 1.52 at baseline to 0.98 after 6 years. These data indicate a significant and clinically relevant reduction in criminal behavior in opioid-dependent patients in long-term maintenance treatment. Maintenance therapy is effective in the reduction in both narcotics-related and acquisition crime.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".