Exploring depression in people with schizophrenia spectrum disorders: A cross-sectional analysis of the clinical relationship with Positive and Negative Syndrome Scale dimensions
Bibliographic record
Abstract
OBJECTIVE: Evidence on the relationship between depression and clinical dimensions of schizophrenia remains limited. This cross-sectional study investigated the association between depression and Positive and Negative Syndrome Scale (PANSS) dimensions in people with schizophrenia spectrum disorders. METHODS: Trained assessors administered the PANSS to measure symptoms of schizophrenia and the Calgary Depression Scale for Schizophrenia to measure depression. The association of depression with overall PANSS score and related dimensions was investigated in multiple logistic regression analyses. RESULTS: We included 231 inpatients with schizophrenia spectrum disorders (mean age: 42.4 (SD: 12.9) years; men: 58.9%; mean overall PANSS score: 82.5 (SD: 20.1); drug-free or naïve: 39.3%), including 78 (33.8%) with clinically significant depressive symptoms. Depression was associated with higher overall (regression coefficient, SE: 0.029, 0.008; p < 0.001) and general psychopathology (regression coefficient, SE: 0.118, 0.023; p < 0.001) PANSS scores. We found an inverse relationship between depression and positive symptoms (regression coefficient, SE: -0.088, 0.028; p = 0.002). No association between depression and negative symptoms was found. CONCLUSION: Despite some limitations, our study shows that people affected by schizophrenia spectrum disorders with depression are likely to show more overall and general psychopathology symptoms but lower positive symptoms. Additional studies are needed to explore the generalizability of our findings.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".