The Complex Latent Structure of Attenuated Psychotic Symptoms: Hierarchical and Bifactor Models of SIPS Symptoms Replicated in Two Large Samples at Clinical High Risk for Psychosis
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: The Structured Interview for Psychosis-Risk Syndromes (SIPS) and other assessments of psychosis risk define clinical high risk for psychosis (CHR) by the presence of attenuated psychotic symptoms. Despite extensive research on attenuated psychotic symptoms, substantial questions remain about their internal psychometric structure and relationships to comorbid non-psychotic symptoms. STUDY DESIGN: Hierarchical and bifactor models were developed for the SIPS in a large CHR sample (NAPLS-3, N = 787) and confirmed through preregistered replication in an independent sample (NAPLS-2, N = 1043). Criterion validity was tested through relationships with CHR status, comorbid symptoms/diagnoses, functional impairment, demographics, neurocognition, and conversion to psychotic disorders. STUDY RESULTS: Most variance in SIPS items (75%-77%) was attributable to a general factor. Hierarchical and bifactor models included a general factor and five specific/lower-order factors (positive symptoms, eccentricity, avolition, lack of emotion, and deteriorated thought process). CHR participants were elevated on the general factor and the positive symptoms factor. The general factor was associated with depressive symptoms; functional impairment; and mood, anxiety, and schizotypal personality diagnoses. The general factor was the best predictor of psychotic disorders (d ≥ 0.50). Positive symptoms and eccentricity had specific effects on conversion outcomes. The deteriorated thought process was least meaningful/replicable. CONCLUSIONS: Attenuated psychotic symptoms, measured by the SIPS, have a complex hierarchical structure with a strong general factor. The general factor relates to internalizing symptoms and functional impairment, emphasizing the roles of general psychopathological distress/impairment in psychosis risk. Shared symptom variance complicates the interpretation of raw symptom scores. Broad transdiagnostic assessment is warranted to model psychosis risk accurately.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".