Depression in Elderly Patients with Schizophrenia
Bibliographic record
Abstract
Background The presence of depressive symptoms impacts negatively the lives of patients suffering from schizophrenia-spectrum disorders. Likewise, the treatment poses many challenges for clinicians. Objectives To specify the profile of elderly with schizophrenia and to evaluate the prevalence of depression and its related factors. Methods A descriptive and analytic study involved 40 elderly patients aged 65 and over with DSM-5 diagnoses of schizophrenia or schizoaffective disorder, followed to the outpatient psychiatry department of Hedi Chaker University Hospital, in Sfax, Tunisia, during the two months of September and October 2015. Positive and negative syndrome scale (PANSS) and Calgary depression scales were used to assess respectively the symptoms of schizophrenia dimensionally and depression. Results The majority of our patients was male (62.5%), single (55%), with low school and socioeconomic level. The mean duration of disease was 45 ± 6.02 years and patients were mostly (90%) in classical neuroleptics. The scale of PANSS showed the predominance of negative symptoms (67.5% of cases). In addition, according to Calgary scale, depression was found in 25% of patients. Factors positively correlated to depression were: the female sex among single (P = 0.043), absence of family support (P = 0.001), treatment with conventional neuroleptics (P = 0.039) and negative symptoms (P = 0.001). Conclusion Depression in patients with schizophrenia is far from exceptional. It is often difficult to diagnose due to the recovery of other symptoms. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 | 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) | 0.001 | 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".