Mapping Psychosis Risk States Onto the Hierarchical Taxonomy of Psychopathology Using Hierarchical Symptom Dimensions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it