Self-reported marijuana use and cardiac arrhythmias (from the Multiethnic Study of Atherosclerosis)
Bibliographic record
Abstract
Marijuana use among all age groups has been increasing, including among older adults aged ≥65 years. There is a lack of epidemiologic data examining arrhythmia risk among users of marijuana. We evaluated cross-sectional associations between current and past marijuana smoking and arrhythmias among 1485 participants from the Multiethnic Study of Atherosclerosis who underwent extended ambulatory electrocardiographic monitoring with the Zio Patch XT. Outcomes included premature atrial contractions, runs of supraventricular tachycardia, premature ventricular contractions, and runs of nonsustained ventricular tachycardia (NSVT). Compared with never users, participants reporting current use of marijuana (n = 40, 3%) had more supraventricular tachycardia/day (adjusted geometric mean ratio [GMR] 1.42, 95% confidence interval [CI] 0.87 to 2.32), more premature atrial contractions/hour (GMR 1.22, 95% CI 0.72, 2.13), and more NSVT/day (GMR 1.28, 95% CI 0.95 to 1.73); although, CIs overlapped 1. Additionally, more frequent marijuana use was associated with more runs of NSVT/day (GMR 1.56, 95% CI 1.13, 2.17). In conclusion, our results suggest that current marijuana use may be associated with a greater burden of arrhythmias. There is a need for additional research, mainly using a prospective design, to clarify if marijuana use causes atrial and ventricular arrhythmias or other cardiovascular complications among older adults.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".