Substance use in individuals at clinical high risk of psychosis
Bibliographic record
Abstract
BACKGROUND: A series of research reports has indicated that the use of substances such as cannabis, alcohol and tobacco are higher in youth at clinical high risk (CHR) of developing psychosis than in controls. Little is known about the longitudinal trajectory of substance use, and findings on the relationship between substance use and later transition to psychosis in CHR individuals are mixed. METHOD: At baseline and 6- and 12-month follow-ups, 735 CHR and 278 control participants completed the Alcohol and Drug Use Scale and a cannabis use questionnaire. The longitudinal trajectory of substance use was evaluated with linear mixed models. RESULTS: CHR participants endorsed significantly higher cannabis and tobacco use severity, and lower alcohol use severity, at baseline and over a 1-year period compared with controls. CHR youth had higher lifetime prevalence and frequency of cannabis, and were significantly younger upon first use, and were more likely to use alone and during the day. Baseline substance use did not differentiate participants who later transitioned to psychosis (n = 90) from those who did not transition (n = 272). Controls had lower tobacco use than CHR participants with a prodromal progression clinical outcome and lower cannabis use than those with a psychotic clinical outcome at the 2-year assessment. CONCLUSIONS: In CHR individuals cannabis and tobacco use is higher than in controls and this pattern persists across 1 year. Evaluation of clinical outcome may provide additional information on the longitudinal impact of substance use that cannot be detected through evaluation of transition/non-transition to psychosis alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".