Prediction of Real-World Functional Disability in Chronic Mental Disorders: A Comparison of Schizophrenia and Bipolar Disorder
Bibliographic record
Abstract
OBJECTIVE: Schizophrenia and bipolar disorder are associated with multidimensional disability. This study examined differential predictors of functional deficits in the two disorders. METHOD: Community-dwelling individuals with schizophrenia (N=161) or bipolar disorder (N=130) were assessed with neuropsychological tests, symptom measures, and performance-based social and adaptive (i.e., everyday living skills) functional competence measures as well as three domains of real-world functioning: community and household activities; work skills; and interpersonal relationships. The authors used confirmatory path analysis to find the best-fitting models to examine the direct and indirect (as mediated by competence) prediction of the three domains of real-world functioning. RESULTS: In all models for both groups, neurocognition's relationship with outcomes was largely mediated by competence. Symptoms were negatively associated with outcomes but unassociated with competence, with the exception of depression, which was a direct and mediated (through social competence) predictor in bipolar disorder. In both groups, neurocognition was related to activities directly and through a mediated relationship with adaptive competence. Work skills were directly and indirectly (through mediation with social competence) predicted by neurocognition in schizophrenia and entirely mediated by adaptive and social competence in bipolar disorder. Neurocognition was associated with interpersonal relationships directly in the schizophrenia group and mediated by social competence in both groups. CONCLUSIONS: Although there was greater disability in schizophrenia, neurocognition predicted worse functioning in all outcome domains in both disorders. These results support the shared role of neurocognition in bipolar disorder and schizophrenia in producing disability, with predictive differences between disorders in domain-specific effects of symptoms and social and adaptive competence.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".