Bibliographic record
Abstract
Purpose: The widespread operationalization of the clinical high-risk (CHR) phase and its clear vulnerability for a first episode of psychosis (FEP) have led to a much-needed focus on factors related to conversion from CHR to FEP.Yet they have also resulted in an assumption which has gone largely unexamined: that CHR invariably precedes FEP.We examined this assumption, and whether key treatment and service outcomes in FEP services vary depending on whether or not subjects experienced a pre-onset CHR state.Methods: Retrospective information was systematically extracted regarding sociodemographic characteristics, psychiatric/behavioral changes, and help-seeking in 200 FEP patients (aged 14-35) receiving specialized early intervention services in Montreal, Canada.Individuals were then followed for 1 year in order to assess individual-and service-level outcomes.Results: At intake to FEP services, 68% of youth recalled a pre-onset CHR syndrome while 32% did not; these groups showed no sociodemographic or clinical differences at baseline.However, duration of untreated psychosis was significantly longer in those with prior CHR syndromes (U = 3315, p = 0.043).Furthermore, symptomatic and functional outcomes were poorer for the prior CHR subgroup in positive symptoms (SAPS group effect, F[1,198] = 4.79, p = 0.03), with significant group-by-time interactions for negative symptoms (SANS, F[1,198] = 5.67, p = 0.018), global functioning (GAF, F[1,198] = 7.96, p = 0.005), and social/occupational functioning (SOFAS, F[1,198] = 4.392, p = 0.037).Conclusions: Most FEP patients experience pre-onset CHR syndromes, enduring greater delays in accessing treatment and poorer longitudinal outcomes than those without prior CHR symptoms.These findings strongly argue for the personalization of early intervention efforts based on knowledge regarding trajectories to 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.759 | 0.601 |
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; the direct Gemma label and the distilled Codex classifier 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".