Recent cannabis use and myocardial infarction in young adults: a cross-sectional study
Bibliographic record
Abstract
Background: Cannabis use is increasing among young adults, but its effects on cardiovascular health are poorly understood. We aimed to assess the association between recent cannabis use and history of myocardial infarction (MI) in young adults (aged 18–44 yr). Methods: We performed a cross-sectional study using pooled data from the 2017 and 2018 cohorts of the American Behavioral Risk Factor Surveillance System survey of US adults. We analyzed the association between any recent cannabis use and history of MI using a weighted logistic regression model that adjusted for demographic factors, socioeconomic factors, health-related behaviours, concomitant substance use and other comorbidities. We also assessed this association after stratifying by frequency of use and by primary method of consumption. Results: Among 33 173 young adults (18.5 million weighted), 4610 respondents (3.2 million weighted) reported recent cannabis use (17.5%, 95% confidence interval [CI] 16.8%–18.2%). A history of MI was more frequent among recent cannabis users (n = 61 of 4610, 1.3%) relative to nonusers (n = 240 of 28 563 [0.8%], adjusted odds ratio [OR] 2.07, 95% CI 1.12–3.82). A history of MI was associated with cannabis use of more than 4 times per month (adjusted OR 2.31, 95% CI 1.18–4.50), and with smoking as a primary method of consumption (adjusted OR 2.01, 95% CI 1.02–3.98). Interpretation: Our study provides evidence supporting an association between recent cannabis use and history of MI in young adults. Increasing cannabis use in an at-risk population could have negative implications for cardiovascular health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".