Potential benefits of quetiapine in the treatment of substance dependence disorders
Bibliographic record
Abstract
OBJECTIVE: Some antipsychotic medications prescribed for the treatment of psychoses, mood disorders or post-traumatic stress disorder in patients with coexisting substance dependence disorders (SDD) have reduced substance dependence. We studied the potential benefits of quetiapine in the treatment of SDD. METHODS: We conducted a retrospective chart review of data for 9 patients who were admitted to a 28-day residential rehabilitation program designed for individuals with SDD during a 3-month period from January 2003 through March 2003 and treated with quetiapine for nonpsychotic anxiety. These patients also met the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, criteria for alcohol, cocaine and/or methamphetamine dependence and substance-induced anxiety disorder. The patients were assessed using the Hamilton-D Rating Scale for Depression (Ham-D), a 10-point Likert scale to measure alcohol or drug cravings, and random Breathalyzer and urine drug screens. RESULTS: Quetiapine was generally well tolerated. Only 1 of the 9 patients stopped taking the medication because of increased anxiety. Other patients reported improvement in sleep and anxiety. The mean decrease in Ham-D score at discharge for the responders was 18.5 (p < 0.005). The biggest decreases on the Ham-D occurred on the subscales of insomnia, agitation, somatic anxiety, psychologic anxiety, hypochondriasis and obsessional symptoms. The mean decrease in the Likert 10-point craving scale was 5.9 for the responders (p < 0.005). These patients' periodic Breathalyzer and urine test results suggested that they remained abstinent from alcohol and other drug use. CONCLUSION: Quetiapine was beneficial in the treatment of SDD in patients with nonpsychotic anxiety.
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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 | 0.000 |
| 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".