Relative Risk of All‐Cause Mortality Associated With Cannabis Use: A Systematic Review and Meta‐Analysis of Cohort Studies
Bibliographic record
Abstract
Background and Aims: Cannabis use has high prevalence and health burden. Although the effects of cannabis use have been studied in the literature, no systematic review and meta-analysis has measured its association with all-cause mortality. The aim of this systematic review and meta-analysis was to systematically synthesize the evidence on association between cannabis use and all-cause mortality. Methods: Following the preregistered protocol (PROSPERO: CRD42023396915), we searched in Scopus, PubMed, Web of Science and ProQuest databases until end of October 2023. We included cohort studies comparing individuals using versus not using cannabis and measuring the association with all-cause mortality. A random-effect meta-analysis was conducted calculating the risk ratio (RR) and 95% confidence interval (CI). Heterogeneity and publication bias were measured. Sensitivity and meta-regression analyses were conducted. The Newcastle Ottawa Scale (NOS) was used to assess study quality. Results: : 0.38). Significantly different RR was observed in prospective versus retrospective designs (2.07 vs. 1.11); cohorts of the general population versus patients (2.53 vs. 1.03). Study sample size was a significant moderator of the association between cannabis use and all-cause mortality, with larger sample size being associated with smaller effect size and less heterogeneity. Based on GRADE assessment, observational evidence, with unadjusted estimates, high heterogeneity with inconsistent results, the overall certainty of evidence seems to be low. Conclusion: Cannabis use was associated with an increased risk of all-cause mortality in the general population but not in patients with severe underlying medical co-morbidities. It should be noted that the evidence may currently be biased and new methodologically strong studies need to be conducted.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".