An overview of the adverse effects of cannabis use for Canadian physicians
Bibliographic record
Abstract
PURPOSE: Cannabis is the most widely used illicit substance and one of the most commonly used psychoactive substances in the world, preceded only by alcohol, tobacco and caffeine. Recent changes in legislation regarding cannabis use in Canada and potential upcoming changes worldwide may have a further impact on the prevalence of cannabis use. Thus, it is critical to understand the risks and potential adverse health effects of acute and long-term cannabis use. Current literature is lacking in many areas surrounding cannabis use, and for the most part is unable to provide clear associations once confounding variables are considered. Here we provide a general overview of the history of cannabis, the physical and mental health consequences, and the risks to specific groups. SOURCE: A scoping search of published articles in PubMed from the start date (1946) until 2018. PRINCIPAL FINDING: Current evidence supports an association between cannabis use and mild respiratory and cardiac effects, but no clear increased risk of cancer. Psychiatric disorders, including schizophrenia and anxiety, show associations with cannabis use; however, a causal effect of cannabis use is unclear. While no evidence for increased risk in pregnancy has been found, risk is still undetermined. Youth may be at a greater risk as earlier initiation of use increases the risk of adverse health effects. CONCLUSION: Overall, evidence for direct and long-term adverse effects of cannabis use is minimal and additional longitudinal studies will be required to better delineate unidentified effects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".