Joint-trajectories of clinical severity, social functioning and cannabis use in first-episode psychosis: A 5-year longitudinal study in 2 urban early intervention services
Bibliographic record
Abstract
• The role of cannabis use in first-episode psychosis may be complex and vary significantly across patients and over time. • Using 5-year longitudinal data, we modelled joint-trajectories of cannabis use and clinical outcomes. • Persistent and severe cannabis use was associated with worse outcomes but also with increased likelihood of child trauma and social adversity. Cannabis use is associated with increased psychosis incidence alongside worse outcomes. The role of cannabis may be complex, vary across patients and over time. Yet, few have examined the longer-term trajectories of cannabis use, symptoms and functioning and their inter-relationships. We conducted a 5-year longitudinal study to estimate joint-trajectories of clinical severity, social functioning, and cannabis use via group-based multi-trajectory modelling on a sample of 395 incident FEP cases. Associations of trajectories with socio-demographic and clinical factors were tested using multinomial regression. The best-fitting model identified 5 joint-trajectories. A first group ( N = 93,23.7 %) presented only marginal improvement despite not using cannabis, while a second with no cannabis use and a third group with low-decreasing use showed clinical amelioration. Among those with baseline harmful cannabis use, a fourth group progressively discontinued use and improved clinically ( N = 78,19.9 %). A fifth group with continued use did not significantly improve over follow-up ( N = 74,18.8 %), and also had the highest odds of homelessness (OR = 22.5,95 %CI = 6.25–81.1) and childhood adversities (OR = 2.25,95 %CI = 1.71–2.97). There is substantial heterogeneity in the joint-trajectories of cannabis use and FEP outcomes. Our findings support the need for intervention aimed at cannabis reduction among heavy users. Multi-disciplinary, trauma-informed interventions may benefit those with persistent cannabis use, given its associations with childhood and social adversity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".