PSYSCAN multi-centre study: baseline characteristics and clinical outcomes of the clinical high risk for psychosis sample
Bibliographic record
Abstract
Predicting outcomes in individuals at clinical high risk (CHR) of developing psychosis remains challenging using clinical metrics alone. The PSYSCAN project aimed to enhance predictive value by integrating data across clinical, environmental, neuroimaging, cognitive, and peripheral blood biomarkers. PSYSCAN employed a naturalistic, prospective design across 12 sites (Europe, Australia, Asia, Americas). Assessments were conducted at baseline, 3, 6, and 12 months, with follow-ups at 18 and 24 months to evaluate clinical and functional outcomes. The study included 238 CHR individuals and 134 healthy controls (HC). At baseline, CHR and HC groups differed significantly in age, education, IQ, and vocational and relationship status. Cannabis and tobacco use did not significantly differ between groups, however CHR individuals had higher proportion of moderate to high risk of tobacco abuse. A substantial portion of the CHR sample met DSM criteria for anxiety (53.4%) and/or mood disorders (52.9%), with some prescribed antidepressants (38.7%), antipsychotics (13.9%), or benzodiazepines (16.4%). Over the follow-up period, 25 CHR individuals (10.5%) transitioned to psychosis. However, the CHR group as a whole showed improvements in functioning and attenuated psychotic symptoms. Similar to other recent multi-centre studies, the CHR cohort exhibits high comorbidity rates and relatively low psychosis transition rates. These findings highlight the clinical heterogeneity within CHR populations and suggest that outcomes extend beyond psychosis onset, reinforcing the need for broader prognostic models that consider functional and transdiagnostic outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".