Consensus paper of the WFSBP task force on cannabis, cannabinoids and psychosis
Bibliographic record
Abstract
OBJECTIVES: The liberalisation of cannabis laws, the increasing availability and potency of cannabis has renewed concern about the risk of psychosis with cannabis. METHODS: The objective of the WFSBP task force was to review the literature about this relationship. RESULTS: Converging lines of evidence suggest that exposure to cannabis increases the risk for psychoses ranging from transient psychotic states to chronic recurrent psychosis. The greater the dose, and the earlier the age of exposure, the greater the risk. For some psychosis outcomes, the evidence supports some of the criteria of causality. However, alternate explanations including reverse causality and confounders cannot be conclusively excluded. Furthermore, cannabis is neither necessary nor sufficient to cause psychosis. More likely it is one of the multiple causal components. In those with established psychosis, cannabis has a negative impact on the course and expression of the illness. Emerging evidence also suggests alterations in the endocannabinoid system in psychotic disorders. CONCLUSIONS: Given that exposure to cannabis and cannabinoids is modifiable, delaying or eliminating exposure to cannabis or cannabinoids, could potentially impact the rates of psychosis related to cannabis, especially in those who are at high risk for developing the disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".