Latent Trajectories of Positive, Negative Symptoms and Functioning in Early Intervention Services for First-Episode Psychosis: A 2-Year Follow-Up Study
Bibliographic record
Abstract
BACKGROUND: From the first episode (FEP), the course of psychosis is marked by substantial heterogeneity of clinical and functional outcomes which poses significant challenges in providing prognostic guidance to patients and families. To better understand such heterogeneity within the context of early intervention services (EIS), this study aimed to examine latent trajectories of positive and negative symptoms and functioning among FEP individuals undergoing EIS. STUDY DESIGN: The Prevention and Early Intervention Program for Psychoses (PEPP-Montreal) is a 2-year EIS for FEP that conducted longitudinal assessments of 689 individuals aged 14-35, including sociodemographics, cognition, psychopathology, and functioning. Latent growth mixture modeling was used to identify distinct patterns of clinical and functional trajectories. The inter-relationship between trajectories, and the association of trajectory membership with baseline characteristics and distal outcomes were investigated using the manual 3-step approach. STUDY RESULTS: Two positive symptom trajectories (Stable-low-32%, Fluctuating-68%,), 3 negative symptom trajectories (Decreasing-41%, Fluctuating-15%, and Stable-high-44%), and 2 functioning trajectories (Increasing-57%, Stable-moderate-43%) were identified. Early treatment response, particularly on negative symptoms, consistently and strongly predicted better outcome trajectories (OR = [3.4-5.5]). Trajectories of higher symptom severity were associated with trajectory of worse functioning (RR = [1.5-2.2]), which exhibited lower rates of clinical and functional remission. CONCLUSION: These findings offer insights into clinically meaningful subgroups of individuals that could inform the prognosis of FEP and the development of individually tailored EIS. Individuals who do not show early improvement in negative symptoms may benefit from earlier psychosocial interventions specifically targeting actionable factors that contribute to secondary negative symptoms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".