Cannabis Use in a First Onset Psychosis Sample: Prevalence and Clinical Differences in Relation to Age of Onset
Bibliographic record
Abstract
Introduction There is a wide range of studies focusing on the use of cannabis in first episode psychosis (PEP). Literature using child and adolescent samples is scarce. Objectives and aims To determine the prevalence and clinical differences between cannabis users and non-cannabis users of early onset first episode psychosis (EOP), and adult onset first episode psychosis (AOP). Method One hundred and forty patients were recruited in adult (AOP subsample, n = 69) and child and adolescent (EOP subsample, n = 71) mental health services. The Positive and Negative Syndrome Scale was used for psychotic symptoms and the Calgary Scale for affective symptoms. The Chi2 test analysed clinical differences between users and nonusers within subsamples, and in the total sample a Pearson correlation was used for the relationship between age at cannabis use and PEP. Results The prevalence of lifetime use of cannabis and the average age at first use were 48% and 13.82 years (± 1.15) in the EOP subsample, and 58% and 17.78 years (± 3.93) in the AOP subsample. Within EOP, cannabis users were older (P = .001), had fewer negative symptoms (P = .045) and less depressive symptoms (P = .005). Within AOP, cannabis users were younger (P = .018) and had greater severity of positive symptoms (P = .021). Age at first cannabis use and age at PEP were positively correlated. Conclusions Cannabis use is prevalent in adult and early onset psychosis. Cannabis users differ clinically from non-users, and the earlier the use of cannabis, the earlier the onset of psychosis. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".