Cannabis use increases the risk of young people developing psychotic symptoms, particularly if already predisposed
Bibliographic record
Abstract
Henquet C, Krabbendam L, Spauwen J, et al . Prospective cohort study of cannabis use, predisposition for psychosis, and psychotic symptoms in young people. BMJ 2004;330:11–14. Q Does cannabis use increase the risk of developing psychotic symptoms in young people with or without a predisposition for psychosis? ### ![Graphic][1] Design: Prospective cohort study. ### ![Graphic][2] Follow up period: Four years. ### ![Graphic][3] Setting: The city and counties of Munich, Germany ### ![Graphic][4] People: 2437 young people aged 14–24 years with or without a predisposition for psychosis and born between 1 June 1970 and 31 May 1981. ### ![Graphic][5] Risk factors: Participants were assessed for cannabis use and for psychosis and predisposition to psychosis by the Munich version of the composite international diagnostic interview (M-CIDI), at baseline and four years follow up. Logistic regression was used to calculate odds ratios and … [1]: /embed/inline-graphic-1.gif [2]: /embed/inline-graphic-2.gif [3]: /embed/inline-graphic-3.gif [4]: /embed/inline-graphic-4.gif [5]: /embed/inline-graphic-5.gif
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".