Usporedba depresivnoga i demoralizacijskoga sindroma kao predskazatelja suicidalnosti u kroničnoj fazi shizofrenije
Bibliographic record
Abstract
The aim of this study was to evaluate the impact of depression and demoralization syndromes \non suicidality in patients with chronic schizophrenia. \nMethods: A prospective study on 146 out-patients with chronic schizophrenia, treated in \nPsychiatric University Hospital “Vrapče”, Zagreb, Croatia, from April 15th 2011 to June 1st \n2013. Every patient’s schizophrenia diagnosis was independently made by two psychiatrists, \nusing ICD-10 and DSM-IV criteria. Sociodemographic, clinical and hospital data were \ncollected for every patient. Severity of their symptoms was assessed with the Positive and \nNegative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS), \nthe InterSePT Scale for Suicidal Thinking (ISST) and the self-administered Demoralization \nScale (DS). Statistical analysis included descriptive statistics, chi-square, Wilcoxon test and \nMcNemar test. Spearman's rank correlation coefficient were calculated to identify and test the \nstrength of relationship between scores on used scales while the multiple regression model \nwas used to test multivariate predictive models of suicidality. \nResults: The statistically significant positive correlations were found between patient’s \ndemoralization categories and their male sex, number of previous hospitalizations (second \ntest), hereditary of suicidality, CDSS score, ISST score and scores on cognitive, affective and \nnegative factors of the PANSS. \nThe statistically significant positive correlation were found between patient’s depression \ncategories and their heredity of suicidality, number of previous hospitalizations, DS score, as \nwell as scores of cognitive, affective and negative factors of the PANSS. \nSuicidality had statistically significant positive correlation with patient’s male sex, heredity of \nsuicidality, number of previous hospitalizations, DS score, CDSS score and scores of \ncognitive, affective and negative factors of the PANSS. \nIn the multiple regression model, two initial predictive variables had statistically significant \nassociation with suicidality: DS score and score of affective factor of the PANSS. In second \ntest, two predictive variables showed statistically significant association with suicidality: DS \nscore and CDSS score. Demoralization was stronger predictor than 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".