Alexithymia in schizophrenia:association with executive function and emotional disorder
Bibliographic record
Abstract
Objective To explore the alexithymia and its relationship with executive function,anxiety and depressive emotion in patients with schizophrenia.Methods 150 schizophrenic patients were assessed with Toronto Alexithymia Scale-20(TAS-20),Wisconsin Card Sorting Test(WCST),Self-rating Anxiety Scale(SAS),Self-rating Depression Scale and Depression Status Inventory(SDS).Patients were divided into high alexithymia group(38 cases with score of TAS-20 ≥62) and low alexithymia group(42 cases with score of TAS-20 ≤53).Score of WCST was compared between the two group.The relationship between scores of WCST,SAS,SDS and TAS-20 were detected by using correlation analysis.Results Number of wrong response,non-preservative errors and failing to keep fixed posture was significantly higher in high alexithymia group than those in low alexithymia group(P0.05).While the number of correct classification,number of correct response,the percentage of correct response and response rate of conceptualization was significantly lower in high alexithymia group than those in low alexithymia group(P0.01).The total score and factor scores of TAS-20 were significantly negatively correlated with WCST performance(P0.05),and were significantly positively correlated with standard scores of SAS and SDS(P0.05).Conclusion The level of alexithymia is closely correlated with the impairment of executive functioning and the negative emotion in schizophrenic patients.
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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".