Mapping Psychosis Risk States Onto the Hierarchical Taxonomy of Psychopathology Using Hierarchical Symptom Dimensions
Bibliographic record
Abstract
Clinical high risk for psychosis (CHR) is a transdiagnostic risk state. However, it is unclear how risk states such as CHR fit within broad transdiagnostic models such as the Hierarchical Taxonomy of Psychopathology (HiTOP). In this study, a hierarchical dimensional symptom structure was defined by unfolding factor analysis of self-report data from 3,460 young adults (mean age = 20.3 years). A subsample ( n = 436) completed clinical interviews, 85 of whom met CHR criteria. Regression models examined relationships between symptom dimensions, CHR status, and clinician-rated symptoms. CHR status was best explained by a reality-distortion dimension, with contributions from internalizing dimensions. Positive and negative attenuated psychotic symptoms were best explained by multiple psychotic and nonpsychotic symptom dimensions, including reality distortion, distress, fear, detachment, and mania. Attenuated psychotic symptoms are a complex presenting problem warranting comprehensive assessment. HiTOP can provide both diagnostic precision and broad transdiagnostic coverage, making it a valuable resource for use with at-risk individuals.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".