Editorial: Violence and mental health. focus on schizophrenia spectrum and psychotic disorders
Bibliographic record
Abstract
schizophrenia were strongly and positively correlated with premeditation characteristics, while they were not significantly correlated with impulsivity characteristics. These findings could be used in the future to predict the occurrence of premeditated aggression in male patients with schizophrenia through empathy assessments.In their systematic review of machine learning (ML) for predicting violent behavior in schizophrenia spectrum disorders, Parsaei et al. describe the results of an in-depth evaluation of the literature in this field. They find that ML models have produced convincing results, highlighting the importance of their use in advanced diagnostics. The authors report that given the rapid growth in the application of various artificial intelligence tools in medical contexts, it seems likely that in the coming years ML models could also be used to predict violent behaviors in patients with schizophrenia spectrum disorders. These tools could be used for timely preventive interventions, such as providing social support and rehabilitation, adjusting medications, and considering more personalized therapeutic approaches, significantly reducing the burden of violent behavior on patients, healthcare systems, and society in general.Additionally, in a randomized controlled trial, Li Z. et al. analyzed the neurofeedback technique for treating male patients with schizophrenia and impulsive behavior. By combining existing scientific evidence with the results of their study, the authors offer new insights and theoretical foundations for the treatment of impulsive behavior in male patients with schizophrenia, demonstrating that six weeks of systematic neurofeedback treatment significantly improves the severity of impulsive behaviors and reduces aggression in these patients.Shifting the focus to network analysis of clinical characteristics in patients with treatmentresistant schizophrenia (TRS), the work of Li W. et al. describes treatment resistance in schizophrenia as multifactorial. At present, no single definition encompasses all aspects, as the pathogenesis is not well understood and the disease remains poorly characterized. From a symptomatic point of view, positive and negative symptoms are key clinical features of TRS; it also appears that differences in core symptoms between TRS and non-treatment-resistant schizophrenia may partly explain this particular resistance. The authors conclude that managing positive and negative symptoms in TRS remains crucial, with particular attention to negative symptoms and related clinical characteristics.In an in-depth study of cognitive impairment and cortical thickness abnormalities in firstepisode schizophrenia patients who had not previously been treated with medication and who exhibited symptoms of agitation, Liang et al. explored the relationships between agitated behavior, cognitive function, and cortical thickness in first-episode schizophrenia not treated with medication (FESN). Based on the results of their study, the authors report that working memory performed worse in FESN and agitation (FESN+A) patients than in controls; furthermore, cortical thickness of the left paracalcarine gyrus was increased in the FESN and non-agitation (FESN-NA) group compared to the healthy control group. The FESN+A group had greater cortical thickness in the right posterior cingulate cortex (rPCC) than the FESN+NA group. The cortical thickness of the rPCC was negatively correlated with working memory scores in the FESN+A group. The authors conclude that abnormal cortical thickness of the rPCC may be related to agitation behavior and cognitive function in patients with FESN+A, suggesting a potential therapeutic target for agitation behavior and cognitive impairment in schizophrenia.Bravve et al. present a systematic review of suicide risk in patients with aggression in schizophrenia; in their assessment, the authors highlight that suicide is the leading risk factor for mortality among individuals with schizophrenia, with a mortality rate 10 times higher than the general population. In the study conducted on individuals who committed suicide, some showed a high risk of aggression and impulsivity, which allowed these indicators to be considered predictors of suicide risk.Based on the evaluation of the proposed studies and the currently available literature, it is important not to minimize the complexity of psychotic disorders and the suffering involved with them (36)(37)(38)(39). These conditions can profoundly affect a person's perception of reality, emotional regulation, and social functioning (40,41). Therefore, early intervention, integrated care, and social rehabilitation, by any means currently provided by scientific literature (37), including and not only modern applications of virtual reality for rehabilitation purposes, are fundamental to promoting recovery and reducing the risk of social marginalization, which in itself can contribute to increasing vulnerability (42)(43)(44). In conclusion, addressing the link between violence and mental health in the context of the spectrum of schizophrenia and psychotic disorders requires a balanced and unbiased approach (45,46). We must strive to protect public safety without compromising the rights and dignity of people with mental illness. The way forward is a nuanced, compassionate understanding; the integration of such an approach with the most modern preventive and rehabilitative techniques and with pharmacological therapies could effectively enable the prevention of the most marked episodes of violence and the achievement of an increasingly optimal outcome.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".