Bibliographic record
Abstract
OBJECTIVE: To assess whether cannabis use in adolescence and young adulthood is a contributory cause of schizophreniform psychosis in that it may precipitate psychosis in vulnerable individuals. METHOD: We reviewed longitudinal studies of adolescents and young adults that examined the relations between self-reported cannabis use and the risk of diagnosis with a psychosis or of reporting psychotic symptoms. We also reviewed studies that controlled for potential confounders, such as other forms of drug use and personal characteristics that predict an increased risk of psychosis. We assessed evidence for the biological plausibility of a contributory causal relation. RESULTS: Evidence from 6 longitudinal studies in 5 countries shows that regular cannabis use predicts an increased risk of a schizophrenia diagnosis or of reporting symptoms of psychosis. These relations persisted after controlling for confounding variables, such as personal characteristics and other drug use. The relation did not seem to be a result of cannabis use to self-medicate symptoms of psychosis. A contributory causal relation is biologically plausible because psychotic disorders involve disturbances in the dopamine neurotransmitter systems with which the cannabinoid system interacts, as demonstrated by animal studies and one human provocation study. CONCLUSION: It is most plausible that cannabis use precipitates schizophrenia in individuals who are vulnerable because of a personal or family history of schizophrenia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".