Objective: The etiology of suicidal thoughts and behaviors (STBs) is complex, multifactorial, and difficult to predict, which limits suicide prevention efforts. The biopsychosocial model of suicide suggests that risk for suicidal ideation, attempts, and death by suicide emerges through the interplay of biological, psychological, and social factors. Yet, these factors are often studied in isolation and assessed inconsistently in practice, making it difficult for researchers and clinicians to appreciate their combined impact in suicide risk assessment and prevention.Method: Charlotte Wells' film Aftersun was examined through the lens of the biopsychosocial model of suicide. Cinematic depictions of important risk factors for STBs, including adverse childhood experiences, shame, social disconnection, financial difficulties, and alcohol use, were integrated with empirical evidence to illustrate how these factors may interact to influence suicide risk.Results: This analysis identifies several empirically supported risk factors for STBs depicted in Aftersun and illustrates how their co-occurrence may increase vulnerability to death by suicide. The film provides a useful example of how suicide risk may be shaped not by any single factor, but by the accumulation and interaction of multiple biological, psychological, and social vulnerabilities.Conclusion: Aftersun provides trainees, clinicians, and researchers with an accessible illustration of the multifactorial nature of suicide and the importance of considering interacting biopsychosocial risk factors during assessment and prevention efforts.
ObjectiveThis study sought to identify independent factors influencing short-term treatment responses in schizophrenia, covering both positive and negative symptoms, and developed a validated nomogram prediction model. Method: The study examined clinical data from 446 schizophrenia patients diagnosed between 2019 and 2023, focusing on health status, admission vital signs, and initial lab results. Participants were split into training and validation groups (6:4 ratio) and classified as improvers or non-improvers based on a ≥ 30% symptom score reduction. LASSO and multivariable logistic regression analyzed the data, with a nomogram illustrating the prediction model. The Bootstrap method generated the ROC curve, and the AUC was calculated. Calibration and clinical decision curves evaluated the model's discrimination, calibration, and clinical utility. Results: The study involved 446 schizophrenia patients, divided into 267 for training and 179 for validation. Of these, 122 improved in psychiatric symptoms, while 324 did not. Key factors linked to improvement- gender, age, smoking, LDL, homocysteine, and folic acid, were identified using LASSO and logistic regression. These factors formed a predictive model, visualized with nomograms. The model's performance, tested with 1000 Bootstrap iterations, had a C-index of 0.7817. ROC analysis showed an AUC of 0.814 for the training group and 0.7863 for the validation group. Conclusion: Gender, age, smoking habits, LDL, homocysteine, and folic acid independently influence the improvement of psychiatric symptoms in schizophrenia. Predictive models using these factors are effective and clinically valuable, with the resulting nomogram serving as a practical tool for evaluating potential symptom improvement in patients.
