The impact of comorbid psychiatric disorders on methadone maintenance treatment in opioid use disorder: a prospective cohort study
Bibliographic record
Abstract
Objective: There is a significant interindividual variability in treatment outcomes in methadone maintenance treatment (MMT) for opioid use disorder (OUD). This prospective cohort study examines the impact of comorbid psychiatric disorders on continued illicit opioid use in patients receiving MMT for OUD. Methods: Data were collected from 935 patients receiving MMT in outpatient clinics between June 2011 and June 2015. Using linear regression analysis, we evaluated the impact of having a comorbid psychiatric disorder on continued illicit opioid use during MMT, adjusting for important confounders. The main outcome measure was percentage of opioid-positive urine screens for 6 months. We conducted a subgroup analysis to determine the influence of specific comorbid psychiatric disorders, including substance use disorders, on continued illicit opioid use. Results: Approximately 80% of participants had at least one comorbid psychiatric disorder in addition to OUD, and 42% of participants had a comorbid substance use disorder. There was no significant association between having a psychiatric comorbidity and continuing opioid use ( P =0.248). Results from subgroup analysis, however, suggest that comorbid tranquilizer (β=20.781, P <0.001) and cocaine (β=6.344, P =0.031) use disorders are associated with increased rates of continuing opioid use. Conclusion: Results from our study may serve to guide future MMT guidelines. Specifically, we find that cocaine or tranquilizer use disorder, comorbid with OUD, places patients at high risk for poor MMT outcomes. Treatment centers may choose to gear more intensive therapy toward such populations. Keywords: opioid use disorder, methadone, substance abuse, comorbidity, psychiatric disorder
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".