Timeframe for Conversion to Psychosis From Individuals at Clinical High-Risk: A Quantile Regression
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: The time taken for an individual who is at the clinical high-risk (CHR) stage to transition to full-blown psychosis may vary from months to years. This temporal aspect, known as the timeframe for conversion to psychosis (TCP), is a crucial but relatively underexplored dimension of psychosis development. STUDY DESIGN: The sample consisted of 145 individuals with CHR who completed a 5-year follow-up with a confirmed transition to psychosis within this period. Clinical variables along with functional variables such as the Global Assessment of Function (GAF) score at baseline (GAF baseline) and GAF-drop from the highest score in the past year. The TCP was defined as the duration from CHR identification to psychosis conversion. Participants were categorized into 3 groups based on TCP: "short" (≤6 months, ≤33.3%), "median" (7-17 months, 33.3%-66.6%), and "long" (≥18 months, ≥66.6%). The quantile regression analysis was applied. STUDY RESULTS: The overall sample had a median TCP of 11 months. Significant differences among the three TCP groups were observed, particularly in GAF-drop (χ2 = 8.806, P = .012), disorganized symptoms (χ2 = 7.071, P = .029), and general symptoms (χ2 = 6.586, P = .037). Greater disorganized symptoms (odds ratio [OR] = 0.824, P = .009) and GAF-drop (OR = 0.867, P = .011) were significantly associated with a shorter TCP, whereas greater general symptoms (OR = 1.198, P = .012) predicted a longer TCP. Quantile regression analysis demonstrated a positive association between TCP and GAF baseline above the 0.7 quantile and a negative association between TCP rank and GAF drop below the 0.5 quantile. CONCLUSIONS: This study underscores the pivotal role of functional characteristics in shaping TCP among individuals with CHR, emphasizing the necessity for a comprehensive consideration of temporal aspects in early prevention efforts.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.012 |
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; both teacher heads agree on what is shown here.
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".