Clinical evolution of substance use disorder patients during treatment with quetiapine: a 12-week, open-label, naturalistic trial
Bibliographic record
Abstract
OBJECTIVE: Substance use disorders (SUDs) are associated with a variety of psychiatric disorders and mood and behavioral instability. Growing evidence suggests that the atypical antipsychotic quetiapine may be useful in the treatment of SUDs. The primary objective of the current open-label trial was to examine the effects of quetiapine on SUD outcomes in patients entering detoxification. METHODS: Thirty-three nonpsychosis SUD patients participated. Patients received quetiapine for a 12-week beginning in detoxification. Craving, quantities used and psychiatric symptoms were evaluated on baseline and at end point. RESULTS: Out of 33 recruited patients, 26 completed > 9 weeks of treatment. Last observation carried forward (LOCF) analyses revealed that craving, SUD severity and quantities used improved during the study. Psychiatric and depressive symptoms also improved. CONCLUSIONS: Our results cannot be attributed per se to the pharmacological effects of quetiapine owing to the open-label design of the study, the small sample size involved and the fact that patients were involved in an intensive therapy program. Nevertheless, our results indicate that quetiapine may be helpful for the treatment of SUD patients entering detoxification. Controlled studies are warranted to determine whether these results are quetiapine-related.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".