Cannabis Use: A New Risk Behaviour Among Adults With Congenital Heart Disease
Bibliographic record
Abstract
Background Cannabis use has increased in Canada and can be associated with adverse cardiovascular events. Given increased use and accessibility to cannabis, there is a need among clinicians to better understand cannabis use in adults with congenital heart disease. Methods A cross-sectional survey (May to September 2018) was used to investigate cannabis use among 252 patients with adult congenital heart disease in a quaternary care centre. Results Of the 252 patients, 53 (21%) reported using cannabis. The majority of cannabis users were men (62%), between the ages of 25 and 39 years (mean age = 32 ± 16 years), and more likely to use tobacco (n = 9, 17%; P = 0.001) and alcohol (n = 37, 60%; P = 0.001). Significant differences ( P = 0.011) were found between the age of onset for tobacco use among cannabis users (mean age: 16 ± 8 years) and non-cannabis users (mean age: 20 ± 3 years). Users reported consuming cannabis for recreational purposes (n = 29, 55%), anxiety (n = 22, 42%), depression (n = 15, 28%), and pain management (n = 4, 8%). Conclusions This study supports our clinical experience that a high proportion of patients with adult congenital heart disease use cannabis. Cannabis users represent a patient population who may demonstrate less optimal health behaviours, including tobacco and alcohol use. Assessment of cannabis use should be an integral part of risk behaviour and cardiovascular risk profile at each clinic visit. Given the current legalization of cannabis in Canada and the growing increase of cannabis use, educational support should be provided to patients and caregivers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".