A bottom-up model of functional outcome in schizophrenia
Bibliographic record
Abstract
Schizophrenia results in poor functional outcomes owing to numerous factors. This study provides the first test of a bottom-up causal model of functional outcome in schizophrenia, using neurocognition, vocal emotional cognition, alexithymia, and negative symptoms as predictors of functional outcome. We investigated a cross-sectional sample of 135 individuals with schizophrenia and 78 controls. Using a series of structural equation modelling analyses, a single pathway was generated among scores from the MATRICS Consensus Cognitive Battery (MCCB), vocal emotion recognition test, Toronto Alexithymia Scale (TAS), Brief Negative Symptom Scale, and the Personal and Social Performance Scale. The scores for each dimension of the MCCB in the schizophrenia group were significantly lower than that in the control group. The recognition accuracy for different emotions (anger, disgust, fear, sadness, surprise, and satire, but not calm was significantly lower in the schizophrenia group than in the control group. Moreover, the scores on the three dimensions of TAS were significantly higher in the schizophrenia group than in the control group. On path analysis modelling, the proposed bottom-up causal model showed a strong fit with the data and formed a single pathway, from neurocognition to vocal emotional cognition, to alexithymia, to negative symptoms, and to poor functional outcomes. The study results strongly support the proposed bottom-up causal model of functional outcome in schizophrenia. The model could be used to better understand the causal factors related to the functional outcome, as well as for the development of intervention strategies to improve functional outcomes in schizophrenia.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".