Subtypes of aggression in patients with schizophrenia: The role of personality disorders
Bibliographic record
Abstract
BACKGROUND: Research has repeatedly demonstrated that schizophrenia has a small but significant association with violence. It is further recognised that a subgroup of people with such links also have personality disorders, but the extent to which type of violence or aggression varies according to subgroup is less clear. AIM: This study aimed to investigate, among co-morbid cases, if the number or type of personality disorders predicts type of aggression. METHODS: In a cross-sectional study, 108 patients with schizophrenia were assessed for personality disorder, Axis-I diagnosis, verbal IQ, social functioning and type of aggression. RESULTS: Logistic regression revealed that the more personality disorders identified (Cluster B personality disorders compared with Clusters A and C) and anti-social personality disorder compared with other Cluster B disorders significantly predicted premeditated aggression. CONCLUSIONS: These findings suggest that detailed personality assessment should be a routine part of comprehensive assessment of patients with schizophrenia. Improved knowledge of the presence and type of personality disorders may help detect and manage the risk of some types of aggression.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".