Timing, Distribution, and Relationship Between Nonpsychotic and Subthreshold Psychotic Symptoms Prior to Emergence of a First Episode of Psychosis
Bibliographic record
Abstract
Prospective population studies suggest that psychotic syndromes may be an emergent phenomenon-a function of severity and complexity of more common mental health presentations and their nonpsychotic symptoms. Examining the relationship between nonpsychotic and subthreshold psychotic symptoms in individuals who later developed the ultimate outcome of interest, a first episode of psychosis (FEP), could provide valuable data to support or refute this conceptualization of how psychosis develops. We therefore conducted a detailed follow-back study consisting of semistructured interviews with 430 patients and families supplemented by chart reviews in a catchment-based sample of affective and nonaffective FEP. The onset and sequence of 27 pre-onset nonpsychotic (NPS) or subthreshold psychotic (STPS) symptoms was systematically characterized. Differences in proportions were analyzed with z-tests, and correlations were assessed with negative binomial regressions. Both the first psychiatric symptom (86.24% NPS) and the first prodromal symptom (66.51% NPS) were more likely to be NPS than STPS. Patients reporting pre-onset STPS had proportionally more of each NPS than did those without pre-onset STPS. Finally, there was a strong positive correlation between NPS counts (reflecting complexity) and STPS counts (β = 0.34, 95% CI [0.31, 0.38], P < 2 e-16). Prior to a FEP, NPS precede STPS, and greater complexity of NPS is associated with the presence and frequency of STPS. These findings complement recent arguments that the emergence of psychotic illness is better conceptualized as part of a continuum-with implications for understanding pluripotential developmental trajectories and strengthening early intervention paradigms.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".