Long-Term Trajectories of Multidimensional Outcomes in Psychosis Following Early Intervention During the Critical Period: The PEPP-Montreal 10+ Study Protocol
Bibliographic record
Abstract
While early intervention services (EIS) have demonstrated short-term benefits, the long-term maintenance of these gains remains largely unexplored. Despite overall better outcomes, individuals with first-episode psychosis exhibit significant variability in their course of recovery. Understanding the risk and protective factors that shape long-term outcome trajectories is essential to predicting and promoting sustained recovery. The Prevention and Early Intervention Program for Psychoses (PEPP-Montreal) is a well-established, high-fidelity EIS program operating within a universal healthcare system and an epidemiologically defined catchment area in South-West Montréal, Canada. Between 2003 and 2018, PEPP-Montreal conducted a detailed two-year longitudinal assessment of 689 individuals aged 14–35 with first-episode affective or non-affective psychosis. Here, we present the protocol for an extended 10-year follow-up study of social, mental, cognitive, and physical health outcomes, supplemented through linkage with health administrative databases to offer a holistic perspective on long-term outcome trajectories. The primary objective of the study is to model the heterogeneity of long-term trajectories across multiple outcome dimensions over the 10-year follow-up period using data-driven methods. This approach will help distinguish clinically meaningful subgroups, characterize their profiles, and identify early predictors of long-term outcomes while providing insight into the mechanisms of changes within trajectories. In particular, the study will assess whether trajectories shaped during the critical period are sustained over the long term. To our knowledge, this represents the most comprehensive investigation of long-term trajectories following EIS in North America and is expected to lay the groundwork for optimizing EIS and developing personalized interventions.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".