Relationship between Alexithymia, Depression and the Negative Symptoms in Schizophrenia with and without Deficit Syndrome
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare schizophrenia patients with and without Deficit Syndrome (DS) with respect to alexithymia, depression and negative symptoms and to investigate the relationship between these variables. METHOD: A total of 210 schizophrenia patients who joined the study were grouped on the basis of the Schedule for the Deficit Syndrome (SDS). Each patient was evaluated using the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS), the Toronto Alexithymia Scale (TAS) and the UKU - Side Effect Rating Scale (UKU-SERS). RESULTS: The DS group had higher alexithymia scores that were not related to the negative symptoms. The prevalence of depression was significantly lower in the same group. Positive symptoms in the DS group were negatively correlated with the two TAS subscales of difficulty describing and identifying feelings. The negative symptoms scores of all the patients with and without DS correlated positively with the mean score on the TAS subscales. The severity of depressive and the negative symptoms predicted alexithymic symptoms. CONCLUSION: Lack of a correlation between the negative symptoms and alexithymic symptoms in DS suggested that the respective symptoms represented different independent phenomena in schizophrenia. A future study might explore the relationship between alexithymia and negative symptoms in association with cognitive functioning.
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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) | 0.002 | 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".