the Deleterious Effect of Cognitive Impairment, Negative Symptoms and Functional Disability on Quality of Life in Chronic Schizophrenia
Bibliographic record
Abstract
Aims: To examine the relative contributions of psychiatric symptoms, functional disability, neuropsychological functioning and sociodemographic variables to quality of life (QOL) in patients with chronic schizophrenia. Method: We examined 165 hospitalised patients with long term schizophrenia (DSM-IV). Measures of psychiatric symptoms included depression (Calgary depression Scale), insight (David Insight Scale), symptom severity (BPRS) and PANSS (Positive and Negative Symptom Scale). Neuropsychological battery included tests for verbal memory, executive functioning, verbal fluency, working memory, motor speed and processing speed. Functional disability was assessed with the Disability Assessment Schedule (DAS-WHO) and Quality of life was assessed with the Quality of Life Scale. Results: Age, years of evolution, negative symptoms, insight and neuropsychological variables (except motor speed) all were significantly related to level of quality of life. in a multiple regression analysis, entering the neuropsychological functioning, functional disability and negative symptoms generated a model which accounted for a 74.9% of the variance in QOL. Functional disability, as expected, accounted for 56% of the variance, whereas Processing Speed explained an additional 6.2%. Symptom Severity and Verbal Fluency predicted 3.7% and 3.5% of the variance, respectively. Negative symptoms, Verbal Memory and Vocabulary, were also significant predictors in the model, but had less predictive value. However, Positive Symptoms and Sociodemographic Variables did not significantly contribute to predict quality of life. Conclusion: Our findings support the predictive value of neuropsychological functioning, functional disability and severity of negative symptoms in long term quality of life in 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 | 0.003 |
| 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) | 0.001 | 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".