Reducing the dimensions of psychotic illness
Bibliographic record
Abstract
Objective: Psychotic disorders are highly heterogeneous. Understanding relationships between symptoms will be relevant to their underlying pathophysiology. We apply dimensionality-reduction methods to characterize the patterns of symptom clustering and how clusters relate to one another.Methods: We analyzed publicly-available data from 153 participants diagnosed with schizophrenia or schizoaffective disorder (fBIRN Data Repository and the Consortium for Neuropsychiatric Phenomics), as well as 636 first-episode psychosis (FEP) subjects from the Prevention and Early Intervention Program for Psychosis (PEPP-Montreal). In all subjects, the Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) were collected. Multidimensional scaling (MDS) combined with cluster analysis was applied to SAPS and SANS scores across these two groups of participants. Principal component analysis (PCA) was applied to hallucination and delusions items. Results: MDS revealed relationships between items of the SAPS and SANS. Our application of cluster analysis to these results identified: 1 cluster of disorganization symptoms, 2 clusters of hallucinations/delusions, and 2 negative symptom clusters. Despite being at an earlier stage of illness, symptoms in FEP presentations were similarly organized. PCA revealed 5 latent components: 1) passivity delusions, 2) auditory hallucinations, 3) other hallucinations, 4) paranoid/negative affect delusions, and 5) grandiose/religious delusions. Conclusions: While hallucinations and delusions commonly co-occur, we found that their specific themes and content sometimes travel together and sometimes apart. This has important implications, not only for treatment and prognosis, but also for experimental medicine. Our data should further inform the search for causal pathophysiological mechanisms.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".