Predictors of Retention and Drug Use Among Patients With Opioid Use Disorder Transferred to a Specialty “Second Chance” Methadone Program
Bibliographic record
Abstract
Background: Many patients in methadone treatment have difficulty achieving or maintaining drug abstinence, and many clinics have policies that lead to discharging these patients. We designed a pilot "Second Chance" (SC) program for patients scheduled to be discharged from other local methadone clinics to be transferred to our clinic. Aim: Determine whether SC patients' retention and opioid use is related to physical or mental health conditions, non-opioid substance use, or treatment features. Methods: From December 2012 to December 2014, this program enrolled 70 patients who were discharged from other clinics in the area; we were their last remaining option for methadone treatment. Unlike the clinic's standard policies, the treatment focus for SC patients was retention rather than abstinence. This program focused on connection to care (eg, psychiatric services) and enabled patients to continue receiving services despite ongoing substance use. Each patient was assessed at treatment entry and followed until June 2016 to evaluate outcomes. Results: < .05) higher rates of current DSM-IV Axis I psychiatric diagnosis (97% vs 70%), prescriptions for opioids (84% vs 55%) and benzodiazepines (65% vs 27%), and higher methadone doses at admission (58 vs 46 mg) but did not differ significantly in rates of 6-month or 1-year retention (77% and 56%, respectively) or all-drug use (39% positive urine drug screens). Methadone doses >65 mg predicted significantly longer retention and less opioid use, but these effects were not moderated by baseline characteristics. Conclusions: Patients in methadone treatment struggling to achieve abstinence may benefit from retention-oriented harm-reduction programs. Higher methadone doses can improve retention and opioid abstinence despite psychiatric comorbidities. Further work is needed to improve program implementation and outcomes in this complex population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".