Prevalence, Perceptions, and Patterns of Cannabis Use Among Cardiac Inpatients at a Tertiary-Care Hospital: A Cross-Sectional Survey
Bibliographic record
Abstract
Background: Cannabis use may adversely affect cardiovascular health. Patterns of use by cardiac patients are unknown. We evaluated the prevalence, perceptions, and patterns of cannabis use among cardiac inpatients. Methods: A consecutive cross-section of cardiac inpatients, hospitalized between November 2019 and May 2020, were surveyed in-person or via telephone. Descriptive statistics and logistic regression were used to examine the characteristics of cannabis use. Results: The prevalence of past-12-month cannabis use was 13.8% (95% confidence interval [CI]: 11.8%-16.0%). Characteristics independently associated with cannabis use were as follows: age < 64 years (< 44 years, odds ratio [OR] = 3.96 [95% CI: 1.65-9.53]; age 45-64 years, OR = 2.72 [95% CI: 1.65-4.47]); tobacco use in the previous 6 months (OR = 1.91 [95% CI: 1.18-3.07]); having a cannabis smoker in one's primary social group (OR = 4.17 [95% CI: 2.73-6.38]); and a history of a mental health diagnosis (OR = 1.82 [95% CI: 1.19-2.79]). Among those using cannabis, 70.5% reported smoking or vaping it; 47.2% reported daily use. Most did not know the tetrahydrocannabinol (THC; 71.6%) or cannabidiol (CBD; 83.3%) content of their cannabis, or the dose of cannabis in their edibles (66.7%). As defined by Canada's Lower Risk Cannabis Use Guidelines, 96.7% of cannabis users reported ≥ 1 higher-risk use behaviour (mean = 2.3, standard deviation = 1.2). Over 60% of patients expressed no intention to quit or reduce cannabis use in the next 6 months. Conclusions: Cannabis use appears prevalent among cardiac patients. Most users demonstrated higher-risk use behaviours and low intentions to quit. Further work is needed to understand the impacts of cannabis use on the cardiovascular system and to develop guidelines and educational tools relating to lower-risk use, for cardiac patients and providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".