Psychosis and Comorbid Opioid Use Disorder: Characteristics and Outcomes in Opioid Substitution Therapy
Bibliographic record
Abstract
Abstract Background and Objectives Substance use disorders are highly prevalent among individuals with psychotic disorders and are associated with negative outcomes. This study aims to explore differences in characteristics and treatment outcomes for individuals with psychotic disorders when compared with individuals with other nonpsychotic psychiatric disorders enrolled in treatment for opioid use disorder (OUD). Methods Data were collected from a prospective cohort study of 415 individuals enrolled in outpatient methadone maintenance treatment (MMT). Psychiatric comorbidity was assessed using the Mini-International Neuropsychiatric Interview. Participants were followed for 12 months. Participant characteristics associated with having a psychotic disorder versus another nonpsychotic psychiatric disorder were explored by logistic regression analysis. Results Altogether, 37 individuals (9%) with a psychotic disorder were identified. Having a psychotic disorder was associated with less opioid-positive urine drug screens (odds ratio [OR] = 0.97, 95% confidence interval [CI] = 0.95, 0.99, P = .046). Twelve-month retention in treatment was not associated with psychotic disorder group status (OR = 0.73, 95% CI = 0.3, 1.77, P = .485). Participants with psychotic disorders were more likely to be prescribed antidepressants (OR = 2.12, 95% CI = 1.06, 4.22, P = .033), antipsychotics (OR = 3.57, 95% CI = 1.74, 7.32, P = .001), mood stabilizers (OR = 6.61, 95% CI = 1.51, 28.97, P = .012), and benzodiazepines (OR = 2.22, 95% CI = 1.11, 4.43, P = .024). Discussion and Conclusions This study contributes to the sparse literature on outcomes of individuals with psychotic disorders and OUD-receiving MMT. Rates of retention in treatment and opioid use are encouraging and contrast to the widely held belief that these individuals do more poorly in treatment. Higher rates of coprescription of sedating and QTc-prolonging medications in this group may pose unique safety concerns.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".