The adverse health effects and harms related to marijuana use: an overview review
Bibliographic record
Abstract
BACKGROUND: With impending marijuana legislation in Canada, a broad understanding of the harms associated with marijuana use is needed to inform the clinical community and public, and to support evidence-informed public policy development. The purpose of the review was to synthesize the evidence on adverse health effects and harms of marijuana use. METHODS: We searched MEDLINE, The Cochrane Database of Systematic Reviews, Embase, PsycINFO, the Cumulative Index to Nursing and Allied Health Literature, and the Health Technology Assessment Database from the inception of each database to May 2018. Given that systematic reviews evaluating one or other specific harm have been published, this is an overview review with the primary objective of assessing a health effect or harm. Data on author, country and year of publication, search strategy and results, and outcomes were extracted. Quality was assessed using the AMSTAR (A Measurement Tool to Assess Systematic Reviews) checklist. RESULTS: The final analysis included 68 reviews. Evidence of harm was reported in 62 reviews for several mental health disorders, brain changes, cognitive outcomes, pregnancy outcomes and testicular cancer. Inconclusive evidence was found for 20 outcomes (some mental health outcomes, other types of cancers and all-cause mortality). No evidence of harm was reported for 6 outcomes. INTERPRETATION: Harm was associated with most outcomes assessed. These results should be viewed with concern by physicians and policy-makers given the prevalence of use, the persistent reporting of a lack of recognition of marijuana as a possibly harmful substance and the emerging context of legalization for recreational use.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".