Effects of comorbid alexithymia on cognitive impairment in chronic schizophrenia: a large-sample study on the Han Chinese population
Bibliographic record
Abstract
Background: Alexithymia and cognitive dysfunction are common in patients with schizophrenia. However, only a few studies have investigated the cognitive performance of patients with schizophrenia and comorbid alexithymia. This study aimed to investigate the relationship between alexithymia and neurocognitive impairment in patients with schizophrenia. Methods: A total of 695 patients who met the DSM-IV diagnostic criteria for schizophrenia were included in this cross-sectional study (male/female = 464/231). Demographic and clinical data were collected using self-reported questionnaires. The severity of alexithymia was assessed using the Toronto Alexithymia Scale (TAS-20), cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) tool, and the severity of psychiatric symptoms was assessed using the Positive and Negative Syndrome Scale (PANSS). Results: < 0.05 for all). In addition, patients with alexithymia had more severe deficits in immediate memory, delayed memory, and language and lower RBANS scores than those without alexithymia. Stepwise multivariate regression analysis showed that alexithymia was a risk factor for language deficits and indicated low total RBANS scores in patients with schizophrenia. Conclusion: This study suggests that patients with chronic schizophrenia with alexithymia have poorer cognitive function than those without alexithymia. Some demographic characteristics and alexithymia are risk factors for cognitive dysfunction in patients with chronic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".