Association of Smoking Cannabis With Cardiovascular Events Among Veterans With Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Whether cannabis is a risk factor for cardiovascular events is unknown. We examined the association between smoking cannabis and cardiovascular events in a cohort of older veterans (66 to 68 years of age) with coronary artery disease. METHODS: The THC Cohort (Heart and Cannabis) comprised 4285 veterans (mean [SD] age, 67.5 [1.01] years; 2% female) with coronary artery disease who were born in 1950 to 1952. Participants were recruited between April 5, 2018, and March 12, 2020, interviewed about health behaviors, and then classified according to their self-reported cannabis smoking status in the previous 30 days. In a separate analysis, we classified participants according to any form of cannabis use (smoking, vaping, or edible use) versus nonuse in the past 30 days. Data on demographic, behavioral, and clinical characteristics were collected by telephone interview and from national Department of Veterans Affairs and Medicare data sources. The primary outcome included a composite of fatal and nonfatal stroke, fatal and nonfatal acute myocardial infarction, and cardiovascular death. The follow-up period for each patient extended from the date of their initial interview until the end of study (June 14, 2022). All participants were followed until they experienced an outcome or until the end of the follow-up period. Survey nonresponse weights and propensity score-based weights were used to reduce bias and confounding. Hazard ratios were estimated using cause-specific hazard models. RESULTS: The cohort included 1015 veterans with coronary artery disease who reported smoking cannabis in the previous 30 days and 3122 veterans who did not smoke cannabis in the previous 30 days. Mean follow-up was 3.3 years, and 563 events occurred. Compared with veterans who did not smoke cannabis, smoking cannabis (past 30 days) was not associated with the composite outcome of acute myocardial infarction, stroke, and cardiovascular death (adjusted hazard ratio, 0.87 [95% CI, 0.61-1.24]). Similarly, use of any form of cannabis (smoking, vaping, dabbing, edibles) in the past 30 days was not associated with the composite outcome. CONCLUSIONS: In this cohort of older veterans with coronary artery disease, self-reported cannabis use was not independently associated with increased cardiovascular events over a mean of 3.3 years of follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".