Combinations and Temporal Associations Among Precursor Symptoms Before a First Episode of Psychosis
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Symptoms that precede a first episode of psychosis (FEP) can ideally be targeted by early intervention services with the aim of preventing or delaying psychosis onset. However, these precursor symptoms emerge in combinations and sequences that do not rest fully within traditional diagnostic categories. To advance our understanding of illness trajectories preceding FEP, we aimed to investigate combinations and temporal associations among precursor symptoms. STUDY DESIGN: Participants were from PEPP-Montréal, a catchment-based early intervention program for FEP. Through semistructured interviews, collateral from relatives, and a review of health and social records, we retrospectively measured the presence or absence of 29 precursor symptoms, including 9 subthreshold psychotic and 20 nonpsychotic symptoms. Sequences of symptoms were derived from the timing of the first precursor symptom relative to the onset of FEP. STUDY RESULTS: The sample included 390 participants (68% men; age range: 14-35 years). Combinations of precursor symptoms most frequently featured depression, anxiety, and substance use. Of 256 possible pairs of initial and subsequent precursor symptoms, many had asymmetrical associations: eg, when the first symptom was suspiciousness, the incidence rate ratio (IRR) of subsequent anxiety was 3.40 (95% confidence interval [CI]: 1.79, 6.46), but when the first symptom was anxiety, the IRR of subsequent suspiciousness was 1.15 (95% CI: 0.77, 1.73). CONCLUSIONS: A detailed examination of precursor symptoms reveals diverse clinical profiles that cut across diagnostic categories and evolve longitudinally prior to FEP. Their identification may contribute to risk assessments and provide insights into the mechanisms of illness progression.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".