Gender Differences of Schizophrenia Patients With and Without Depressive Symptoms in Clinical Characteristics
Bibliographic record
Abstract
Objectives: To investigate the differences in psychotic symptoms and cognitive function in schizophrenics with and without depression and to compare gender differences in the correlation between depressive symptoms and clinical characteristics in those patients. Methods: A total of 190 schizophrenia patients and 200 healthy controls were recruited in the study. We used the Positive and Negative Symptom Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) to evaluate the psychiatric symptoms, depressive symptoms and cognitive function, respectively. Patients with CDSS score ≥7 were divided into depression group, and CDSS < 7 was viewed as without depression. Results: Patients with schizophrenia had lower total scores of RBANS and five subscale (immediate memory, visual span, verbal function, attention, and delayed memory) scores compared to healthy controls. In the case group, patients who concomitant with depression had higher PANSS scores (Ps < 0.001) and lower RBANS (Ps < 0.05) scores than those without depression. After gender stratification, PANSS total scores and subscale scores were significantly different between schizophrenics with and without depressive symptoms in both male and female groups (Ps < 0.001). For cognitive function, there were significant differences in RBANS total score and subscale scores except attention between female patients with and without schizophrenia but not in male schizophrenia patients. Furthermore, the correlation analysis showed that the total CDSS score was positively correlated with PANSS score (P < 0.001) and RBANS score in male and female groups (male: P = 0.010, female: P = 0.001). Conclusion: Our findings provided evidence supporting the gender differences in psychiatric symptoms and cognitive function between schizophrenia patients with and without depressive symptoms.
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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 | 0.000 |
| 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) | 0.003 | 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".