The percentage and clinical correlates of alexithymia in stable patients with schizophrenia
Bibliographic record
Abstract
Alexithymia is a common, but less-recognized affective deficit in patients with schizophrenia. To date, no definitive conclusions have been drawn about the relationship between alexithymia and the clinical symptoms or their clinical correlates, particularly in stable patients with schizophrenia. The purpose of this study was to investigate the link between alexithymia and psychopathological symptoms, as well as any associated correlates, in stable patients with schizophrenia. A total of 435 Chinese patients with schizophrenia were recruited. The Positive and Negative Symptoms Scale (PANSS) was used to evaluate each patient's psychopathological symptoms. The Toronto Alexithymia Scale (TAS-20) was used to measure alexithymia. The percentage of alexithymia was 35.2% in stable patients with schizophrenia. Compared to non-alexithymia patients, patients with alexithymia had higher PANSS total scores, negative subscores, depressive subscores, and cognitive subscores (all p < 0.05). Multivariate regression analysis revealed that the following variables were positively associated with TAS-20 total scores: PANSS negative subscores (β = 0.274, t = 3.198, p = 0.001) and PANSS depressive subscores (β = 0.366, t = 2.500, p = 0.013). Education years (β = - 0.453, t = - 2.824, p = 0.005) was negatively associated with TAS-20 total scores. Our results suggest that the percentage of alexithymia was relatively higher in stable patients with schizophrenia. Education levels, negative symptoms, and depressive symptoms were independently associated with alexithymia in this specific population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".