Effects of quetiapine and olanzapine in patients with psychosis and violent behavior: a pilot randomized, open-label, comparative study
Bibliographic record
Abstract
OBJECTIVE: Patients suffering from psychosis are more likely than the general population to commit aggressive acts, but the therapeutics of aggressive behavior are still a matter of debate. METHODS: This pilot randomized, open-label study compared the efficacy of quetiapine versus olanzapine in reducing impulsive and aggressive behaviors (primary endpoints) and psychotic symptoms (secondary endpoints) from baseline to days 1, 7, 14, 28, 42, 56, and 70, in 15 violent schizophrenic patients hospitalized in a maximum-security psychiatric hospital. RESULTS: Quetiapine (525±45 mg) and olanzapine (18.5±4.8 mg) were both efficacious in reducing Impulsivity Rating Scale from baseline to day 70. In addition, both treatments reduced the Brief Psychiatric Rating Scale, Positive and Negative Syndrome Scale, and Clinical Global Impression Scale scores at day 70 compared to baseline, and no differences were observed between treatments. Moreover, quetiapine, but not olanzapine, yielded an improvement of depressive symptoms in the items "depression" in Brief Psychiatric Rating Scale and "blunted affect" in Positive and Negative Syndrome Scale. Modified Overt Aggression Scale scores were also decreased from baseline to the endpoint, but due to the limited number of patients, it was not possible to detect a significant difference. CONCLUSION: In this pilot study, quetiapine and olanzapine equally decreased impulsive and psychotic symptoms after 8 weeks of treatment. Double-blind, large studies are needed to confirm the validity of these two treatments in highly aggressive and violent schizophrenic patients.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".