Effectiveness of quetiapine for the management of aggressive psychosis in the emergency psychiatric setting: a naturalistic uncontrolled trial
Bibliographic record
Abstract
Objective Traditionally, conventional antipsychotics, often administered intramuscularly, are used to reduce hostility and aggression in the emergency setting. This study investigated the efficacy of quetiapine, an oral atypical antipsychotic, in managing aggressive psychosis. Methods Adult hospitalized patients (n=36) with an Overt Aggression Scale (OAS) total score ≥1 and a Brief Psychiatric Rating Scale-derived Psychosis Index score ≥6 received 100-800 mg/day flexibly dosed quetiapine and were monitored daily for a maximum of 5 days. The OAS total score and Physical Aggression Against Others subscale score were primary efficacy assessments. Tolerability was assessed with the Udvalg for Kliniske Undersogelser Side Effects Rating Scale. Results An 83% decrease in the OAS Physical Aggression Against Others subscale score occurred from Day 1 (baseline) to Day 2, which was generally maintained to Day 5; a 39% reduction in OAS total score was observed by Day 2. The OAS total score decreased from a mean baseline of 3.3 to 1.5 at Day 5, a significant decrease for Days 2-4 (P < 0.01) and of borderline significance on Day 5 (P=0.059). Adverse events were mostly mild to moderate with concentration difficulties and somnolence the most common. Conclusion These results from an uncontrolled trial suggest that quetiapine may be helpful for some aggressive patients in the emergency setting.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".