[Distinguishing depressive from nondepressive patients in schizophrenia in terms of symptomatology].
Bibliographic record
Abstract
OBJECTIVE: Schizophrenia is a syndrome with five dimensions and it contains positive symptoms, negative symptoms, disorganization, depression, and cognitive symptoms. Depression which is one of these dimensions can be seen with every dimension and in every phase of schizophrenia. In this study our aim was to examine the overlapping features and the discriminating symptoms of depression comorbid with schizophrenia. METHOD: The subjects consisted of 30 patients diagnosed as schizophrenic with a concurrent depression, disorder, and 30 patients diagnosed as schizophrenic without a concurrent depression, admitted to the outpatient department of two university hospitals. Patients were assessed using the following measures: Calgary Depression Scale for Schizophrenia (CDSS), Scales for Assessment of Positive and Negative Symptoms (SAPS and SANS respectively), and Extrapyramidal Symptom Rating Scale (ESRS). RESULTS: For determining the overlapping features of schizophrenia with depression, symptoms drawn from SAPS, SANS and ESRS were correlated with CDSS score, and affective flattening, apathy, attention, and delusions were significantly correlated with total score of CDSS. By Wilks' Lambda method, the discriminating symptoms of the depressive and schizophrenic patients from the only schizophrenic patients were found to be depressive mood and feelings of worthlessness. CONCLUSION: The shared nature of the overlapping features and discriminating symptoms is the emotional component affecting the presence of depression comorbid with schizophrenia. Negative affectivity is the dominance of negative-loaded feelings in the emotional component. In symptomatology, the presence of negative affectivity in the emotional component is suggested to be a specifier for depression in schizophrenia.
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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 | 0.002 |
| 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".