Subcategories of the Clinical High-Risk State for Psychosis and Their Relationship to a Full First-Episode Psychosis Sample: An Exploratory Analysis of Longitudinal Outcomes
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Subcategories of the Clinical High-Risk state for psychosis (CHR-P) have been associated with differential risk for transition to first-episode psychosis (FEP), but their relevance for longer term FEP outcomes remains unclear. We aimed to determine the prevalence of 2 CHR-P subcategories - attenuated psychotic symptoms (APS) and brief intermittent psychotic symptoms (BIPS) - in a full sample of FEP patients, along with their association with outcome trajectories following psychosis onset. STUDY DESIGN: Participants were recruited from an early intervention service and followed over 2 years, with repeated measures of psychotic symptoms, affective symptoms, and functioning. Pre-onset symptoms were assessed using follow-back methods to reconstruct subgroups and their prevalence within the sample. Linear mixed models were applied to examine associations between putative CHR-P subcategories and longitudinal outcomes. STUDY RESULTS: Of 319 patients, 240 (75.24%) experienced subthreshold psychotic symptoms indicative of a CHR-P state; of these, 51 (21.25%) had potential BIPS (either alone or with APS) and 189 (78.75%) potential APS only. There were no mean differences in scores for psychotic symptoms, affective symptoms, or functioning between subgroups. However, there was a slower improvement in Global Assessment of Functioning (GAF) scores in the putative APS subgroup, which converged with the putative BIPS subgroup by year 2. CONCLUSIONS: Putative CHR-P subcategories of APS and BIPS exhibited similar outcome trajectories beyond psychosis onset, except for a possibly slower functional recovery in the putative APS subgroup. Longer term studies across stages of illness are needed to better understand the prognostic utility of these identifiers after FEP.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".