Clinical risk factors for suicidality in young males with schizophrenia spectrum disorders.
Bibliographic record
Abstract
BACKGROUND: Schizophrenia spectrum disorders (SSD) are linked to a higher risk of suicidality, especially among young adults. Despite progress in psychiatric treatments, suicidality remains a leading cause of early death in this group. Symptoms like depression and anxiety are increasingly seen as major contributors to this risk. This study aims to explore clinical risk factors for suicidality in young males inpatients diagnosed with SSD, focusing on the roles of depression, anxiety, and previous suicidal behavior. METHODS: This cross-sectional study was conducted at the Psychiatric Hospital no. 1 named after N.A. Alexeev of the Department of Health of Moscow, involving 40 male inpatients aged 18-35 years. Participants were divided into two groups: those with suicidal behavior (n=20) and those without (n=20). Psychometric assessments included the Columbia Suicide Severity Rating Scale (C-SSRS), Calgary Depression Scale for Schizophrenia (CDSS), Positive and Negative Syndrome Scale (PANSS), and Personal and Social Performance scale (PSP). Descriptive statistics, correlation analysis, regression analysis, and Student's t-tests were used. RESULTS: The group with suicidal behavior had significantly higher scores on the C-SSRS and CDSS, as well as on the PANSS anxiety/depression subscale, compared to the control group. Regression analysis indicated that depression and anxiety accounted for 74% of the variance in suicidality scores. No significant differences in social functioning (PSP) were found between the groups. A history of suicide attempts was not a significant predictor in this sample. CONCLUSION: Depression and anxiety are significant predictors of suicidality in young males with SSD. Historical suicide attempts showed no significant effect in this sample. The findings underscore the importance of regular screening and timely intervention to lower suicide risk in young adults with SSD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".