Depression and subjective quality of life in chronic phase schizophrenic patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the influence of depression on subjective quality of life in schizophrenic patients. METHOD: Sixty-seven schizophrenic patients in a stabilized phase were included. Schizophrenic symptoms were evaluated using the Positive and Negative Symptoms Scale (PANSS). The subjective quality of life was evaluated using the short version of the Lehman quality of life scale (QoLI). Depression was evaluated using the Calgary Depression Scale for Schizophrenia (CDSS) and extrapyramidal effects with the Extrapyramidal Symptoms Rating Scale (ESRS). RESULTS: The PANSS total score, PANSS general psychopathology subscore, PANSS depression factor, the total CDSS and some ESRS scores were negatively correlated with the overall life satisfaction score. The CDSS score was negatively correlated with all except one QoLI score. QoLI scores were significantly lower in depressed patients, and this result remained consistent for four QoLi dimensions when adjusted on ESRS and PANSS scores. When analysing the association between high depression scores and high parkinsonism scores with reduced quality of life, multivariate analysis showed that depression was the main explanatory factor: the CDSS total score explained 22% of the variance of the overall subjective quality of life score. The patient questionnaire at the ESRS explained 10.5% of the variance of the 'mental and physical health' QoLI score. CONCLUSION: In schizophrenic patients, depressive symptoms should be focused because of their strong association to overall subjective quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".