Relationship between social and cognitive functions in people with schizophrenia
Bibliographic record
Abstract
PURPOSE: The purpose of the present study was to examine clinical factors related to social function in people with schizophrenia. PATIENTS AND METHODS: The participants were 55 stabilized outpatients with schizophrenia. Their mean age was 39.36 (SD =10.65) years. Social function was assessed using the Quality of Life Scale (QLS). Cognitive function was evaluated with the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery (MCCB). Clinical symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia, and the Drug-Induced Extrapyramidal Symptoms Scale. RESULTS: Neither the MCCB cognitive domain score nor composite score was correlated with the QLS scores. However, of the 10 MCCB subtests, the Trail Making Test Part A and the Brief Assessment of Cognition in Schizophrenia-Symbol Coding (BACS-SC) scores were positively correlated with the QLS scores. Among clinical variables, especially the PANSS negative syndrome scale score had a strong negative correlation with the QLS scores. Stepwise regression analyses showed that the PANSS negative syndrome scale score was an independent predictor of the QLS scores, and although the BACS-SC score predicted the QLS common objects and activities subscale score, the association was not so strong compared to the PANSS negative syndrome scale score. CONCLUSION: These results indicate that speed of processing evaluated by BACS-SC could predict some aspect of social function but negative symptoms have a much stronger impact on global social function in people with 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 | 0.000 | 0.002 |
| 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".