The mediating role of depression in pathways linking positive and negative symptoms in schizophrenia. A longitudinal analysis using latent variable structural equation modelling
Bibliographic record
Abstract
BACKGROUND: The interaction between positive, negative and depressive symptoms experienced by people with schizophrenia is complex. We used longitudinal data to test the hypothesis that depressive symptoms mediate the links between positive and negative symptoms. METHODS: We analyzed data from the European Schizophrenia Cohort, randomly sampled from outpatient services in France, Germany and the UK (N = 1208). Initial measures were repeated after 6 and 12 months. Depressive symptoms were identified using the Calgary Depression Scale for Schizophrenia (CDSS), while positive and negative symptoms were assessed with the Positive and Negative Syndrome Scale (PANSS). Latent variable structural equation modelling was used to investigate the mediating role of depression assessed at 6 months in relation to the longitudinal association between positive symptoms at baseline and negative symptoms at 12 months. RESULTS: We found longitudinal associations between positive symptoms at baseline and negative symptoms at 12 months, as well as between both of these and CDSS levels at 6 months. However depression did not mediate the longitudinal association between PANSS scores; all the effect was direct. CONCLUSIONS: Our findings are incompatible with a mediating function for depression on the pathway from positive to negative symptoms, at least on this timescale. The role of depression in schizophrenic disorders remains a challenge for categorical and hierarchical diagnostic systems alike. Future research should analyze specific domains of both depressive and negative symptoms (e.g. motivational and hedonic impairments). The clinical management of negative symptoms using antidepressant treatments may need to be reconsidered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".