Antipsychotic Agents for the Treatment of Substance Use Disorders in Patients With and Without Comorbid Psychosis
Bibliographic record
Abstract
Substance dependence has serious negative consequences upon society such as increased health care costs, loss of productivity, and rising crime rates. Although there is some preliminary evidence that atypical antipsychotic agents may be effective in treating substance dependence, results have been mixed, with some studies demonstrating positive and others negative or no effect. The present study was aimed at determining whether this disparity originates from that reviewers separately discussed trials in patients with (DD) and without (SD) comorbid psychosis. Using electronic databases, we screened the relevant literature, leaving only studies that used a randomized, double-blind, placebo-controlled or case-control design that had a duration of 4 weeks or longer. A total of 43 studies were identified; of these, 23 fell into the category of DD and 20 into the category of SD. Studies in the DD category suggest that atypical antipsychotic agents, especially clozapine, may decrease substance use in individuals with alcohol and drug (mostly cannabis) use disorders. Studies in the SD category suggest that atypical antipsychotic agents may be beneficial for the treatment of alcohol dependence, at least in some subpopulations of alcoholics. They also suggest that these agents are not effective at treating stimulant dependence and may aggravate the condition in some cases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".