Editorial: Violence and mental health. focus on schizophrenia spectrum and psychotic disorders
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,005 |
| Bibliométrie | 0,006 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,018 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,013 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».