Prevalence, Influencing Factors, and Cognitive Characteristics of Depressive Symptoms in Elderly Patients with Schizophrenia
Bibliographic record
Abstract
PURPOSE: To investigate the prevalence, influencing factors, and cognitive characteristics of depressive symptoms in elderly patients with chronic schizophrenia. PATIENTS AND METHODS: A total of 241 elderly patients with chronic schizophrenia and 156 healthy controls were enrolled in this study. The Geriatric Depression Scale (GDS) was used to assess depressive symptoms; the Positive and Negative Syndrome Scale was used to assess psychotic symptoms; and both the Mini-Mental State Examination and Montreal Cognitive Assessment were used to assess overall cognitive function, while the Activity of Daily Living Scale was used to assess daily living ability. RESULTS: The prevalence of depressive symptoms was 48.5% (117/241) in elderly patients with chronic schizophrenia, which was substantially higher than that of normal controls (17.3%, 27/156). Using a stepwise binary logistic regression analysis, we found that high education (p=0.006, odds ratio [OR]=1.122, 95% confidence interval [CI]:1.034-1.218) and hypertension (p=0.019, OR=0.519, 95% CI: 0.300-0.898) were influencing factors for the comorbidity of depressive symptoms. Compared with individuals without depressive symptoms, individuals with depressive symptoms usually display worse overall cognitive function and more severe impairment of activities of daily living, but fewer psychotic symptoms. Interestingly, the GDS score was negatively correlated with the course of the disease (r=-0.157, p=0.016), suggesting that patients who had recently been admitted to the hospital were more likely to develop depression. CONCLUSION: Elderly patients with chronic schizophrenia are often associated with higher levels of depression. Therefore, their overall cognitive function is worse, and their activities of daily living are more seriously impaired. Therefore, these patients should be provided with appropriate psychological comfort, especially those who have recently been admitted to the hospital.
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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 | 0.001 |
| 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) | 0.001 | 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".