[Evaluation of the psychometric properties of the Calgary Depression Scale for Schizophrenics (CDSS) in a Hungarian clinical population of patients with schizophrenia].
Bibliographic record
Abstract
OBJECTIVE: Evaluation of the reliability and validity of the Hungarian version of the Calgary Depression Scale for Schizophrenics (CDSS) in a Hungarian clinical population of patients with schizophrenia. METHOD: One hundred patients diagnosed with schizophrenia according to DSM-IV criteria were included in this study. Patients were all acutely admitted to the psychiatric unit of the Merényi Hospital due to relapse. For evaluating convergent and discriminant validity of the CDSS, scales measuring depression, negative symptoms, extrapyramidal side effects, antipsychotic-induced dysphoria were assessed: Hamilton Depression Rating Scale (HDRS), Positive and Negative Syndrome Scale (PANSS), Subject Well-being under Neuroleptic Treatment (SWN), Drug Induced Extra-Pyramidal Symptoms Scale (DIEPSS), Clinical Global Impressions-Severity (CGI-S), Clinical Global Impressions-Improvment (CGI-I). In order to examine the test-retest reliability of the CDSS we conducted a 3 month-follow-up (n=83), during which we applied the same set of scales. RESULTS: The interrater reliability was high in both the CDSS (ICC= 0.98) and the other scales (ICC= 0.75-0.98). Measures of internal consistency showed strong reliability, Cronbach alfa was 0.87, Guttman split-half reliabilty was 0.82. Correlation calculations between CDSS and HAM-D resulted in high correlation: r=0.75-0.81. CDSS showed lower correlations with PANSS-N, DIEPSS and SWN, indicating that this scale is able to separate symptoms of depression from negative symptoms, extrapyramidal side-effects and antipsychotic-induced dysphoria. CONCLUSION: The Hungarian version of CDSS can be used to assess depression in schizophrenia and we recommend the introduction of the scale into practice in Hungary.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".