Correlation Between Violence and Antipsychotic Dosage in Schizophrenia: A Secondary Analysis of The Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) Dataset
Bibliographic record
Abstract
INTRODUCTION: Violent behavior is more common in individuals with schizophrenia, compared to the general population. Studies suggest higher psychotic symptoms are predictive of greater violent behavior. On the other hand, violent behaviors are reduced with antipsychotic treatment. However, the relationship between antipsychotic dosage and violence has not been studied to date. Thus, we aimed to determine if there exists an association between antipsychotic dosage and violence scores and whether the maximum violence would be predictive of the final antipsychotic dosage. We hypothesized that the violence scores at the final assessment in the Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) would be correlated with the corresponding drug dosage and the maximum violence severity score would be predictive of the final dosage. METHODS: Antipsychotic dosage at the end of the trial was converted into defined daily dosage and chlorpromazine equivalents (CPZe). Final and maximum violence sum scores were analyzed from the final violence assessment interviews. Spearman's rank-order correlation and linear regression analyses were used to analyze the relationship between the violence scores and standardized antipsychotic dosages. RESULTS: The analysis was on 952 individuals with schizophrenia. There was a significant association between maximum violence severity score and the final CPZe dosage (p=0.049). Exploratory analysis of age and ethnicity revealed younger non-white individuals to be at a higher risk of engaging in violent activities. DISCUSSION: Violence in schizophrenia is associated with poor illness course. Further studies focusing on violence in younger non-white individuals are warranted.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".