Is Alexithymia Related to Negative Symptoms of Schizophrenia?
Bibliographic record
Abstract
BACKGROUND: Alexithymic features are close to anhedonia, blunted affect, and alogia that are also characteristics of the negative symptoms of schizophrenia. This study aimed to evaluate whether alexithymia is associated with negative symptoms and is related to the change of schizophrenic symptoms over time. SAMPLING AND METHODS: A consecutive sample of 29 schizophrenic outpatients was evaluated at baseline and at 3, 6, and 12 months during appropriate treatment. They completed the Positive and Negative Syndrome Scale, the Montgomery and Asberg Depression Rating Scale, the Global Assessment of Functioning Scale, and the 20-item Toronto Alexithymia Scale at any time points. RESULTS: The psychiatric scale scores showed significant symptom improvement over time but were unrelated to the alexithymia score that was instead stable over time. Hierarchical regression showed that the 20-item Toronto Alexithymia Scale at baseline was the sole predictor of alexithymia at 12 months, after controlling for psychopathology and psychological functioning. CONCLUSIONS: Alexithymia was unrelated to negative symptoms, suggesting it is an independent and separate construct from schizophrenia. As expected, the negative symptoms were associated instead with illness-related aspects of depression and psychosocial functioning. Caution should be expressed in generalization mainly because this study is limited by the small sample size.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".