Pathways to Aggression in Schizophrenia Affect Results of Treatment
Bibliographic record
Abstract
Schizophrenia elevates the risk for aggressive behavior and violent crime, and different approaches have been used to manage this problem. The results of such treatments vary. One reason for this variation is that aggressive behavior in schizophrenia is heterogeneous in origin. This heterogeneity has usually not been accounted for in treatment trials nor is it adequately appreciated in routine clinical treatment planning. Here, we review pathways that may lead to the development of aggressive behavior in patients with schizophrenia and discuss their impact on treatment. Elements in these pathways include predisposing factors such as genotype and prenatal toxic effects, development of psychotic symptoms and neurocognitive impairments, substance abuse, nonadherence to treatment, childhood maltreatment, conduct disorder, comorbid antisocial personality disorder/psychopathy, and stressful experiences in adult life. Clinicians' knowledge of the patient's historical trajectory along these pathways may inform the choice of optimal treatment of aggressive behavior. Clozapine has superior antiaggressive activity in comparison with other antipsychotics and with all other pharmacological treatments. It is usually effective when aggressive behavior is related to psychotic symptoms. However, in many patients, aggression is at least partly based on other factors such as comorbid substance use disorder, comorbid antisocial personality disorder/psychopathy, or current stress. These conditions which are sometimes underdiagnosed in clinical practice must be addressed by appropriate adjunctive psychosocial approaches or other treatments. Treatment adherence has a crucial role in the prevention of aggressive behavior in schizophrenia 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".