Characterizing Covariant Trajectories of Individuals at Clinical High Risk for Psychosis Across Symptomatic and Functional Domains
Bibliographic record
Abstract
OBJECTIVE: The authors sought to characterize differences in outcomes among help-seeking individuals at clinical high risk for psychosis by identifying covariant longitudinal patterns of symptoms and functioning. METHODS: Group-based multitrajectory modeling was applied to longitudinal ratings of four symptom domains (positive, negative, disorganized, general) and general functioning among clinical high-risk individuals in an initial discovery sample (N=422). An independent sample (N=133) was used to test replicability. RESULTS: Three trajectory groups were identified among clinical high-risk individuals in the discovery sample: group 1 (30%) exhibited substantial improvement across all domains, with half reaching positive outcomes for both functioning and positive symptoms; group 2 (49%) exhibited moderate impairments across domains, with approximately one-quarter meeting criteria for positive outcomes; the remaining participants (group 3; 22%) exhibited consistent levels of severe impairment across domains and did not experience positive outcomes. These trajectory groups and remission patterns were replicated in an independent sample. CONCLUSIONS: Replicable subgroups of help-seeking clinical high-risk cases can be ascertained based on distinctive profiles of change over time in symptoms and functioning. Within each of the three identified subgroups, similar patterns of change (i.e., rapid, moderate, or no improvement) were observed across the four symptom domains and functioning. This consistency of change over time across domains within each subgroup is a novel observation supporting the syndrome consistency of clinical high-risk symptoms and signs. The observed trajectory subgroups are suggestive of different degrees of need for clinical interventions, ranging from minimal or supportive for about one-third of cases to increasingly intensive among the remainder.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".