Relationship Suicide, Cognitive Functions, and Depression in Patients with Schizophrenia
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to compare schizophrenic patients with and without a suicide attempt history in terms of sociodemographic and clinical features and cognitive functions and to determine the predictive factors for suicide attempt history. METHODS: In this study, we assessed and compared 70 patients with schizophrenia, 27 patients with a suicide attempt history, and 43 patients without a suicide attempt history. The cognitive functions of patients were assessed by the Stroop test, Wisconsin Card Sorting Test (WCST), and Rey Auditory Verbal Learning Test. In order to evaluate clinical symptoms, the Positive and Negative Syndrome Scale (PANSS) and Calgary Depression Scale for Schizophrenia (CDSS) were used. RESULTS: In this study, the number of hospitalizations, PANSS general psychopathology subscale score, CDSS total score, suicide item score, and WCST total number of responses (WCST1) were significantly higher among the patients with a suicide attempt history. The WCST1 and CDSS total scores were predicted using the suicide attempt history. CONCLUSION: Revealing the factors related to suicidal behavior in patients with schizophrenia contributes to the prevention of suicide. Studies with long-term follow-up and with a larger sample group are required for the investigation of relationship suicide, cognitive impairment, which is one of the core symptoms of schizophrenia, and depression.
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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 | 0.001 | 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".