Bibliographic record
Abstract
Growing evidence supports the hypothesis that cannabis consumption is a risk factor for the development of psychotic symptoms. Nonetheless, controversy remains about the causal nature of the association. This review takes the debate further through a critical appraisal of the evidence. An electronic search was performed, allowing to identify 622 studies published until June 1st 2005. Longitudinal studies and literature reviews were selected if they addressed specifically the issues of the cannabis/psychosis relationship or possible mechanisms involved. Ten epidemiological studies were relevant: three supported a causal relationship between cannabis use and diagnosed psychosis; five suggested that chronic cannabis intake increases the frequency of psychotic symptoms, but not of diagnosed psychosis; and two showed no causal relationship. Potential neurobiological mechanisms were also identified, involving dopamine, endocannabinoids, and brain growth factors. Although there is evidence that cannabis use increases the risk of developing psychotic symptoms, the causal nature of this association remains unclear. Contributing factors include heavy consumption, length and early age of exposure, and psychotic vulnerability. This conclusion should be mitigated by uncertainty arising from cannabis use assessment, psychosis measurement, reverse causality and control of residual confounding.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".