Patterns of E-Cigarette Use Among Cardiac Inpatients at a Tertiary-Care Hospital: A Cross-Sectional Survey
Bibliographic record
Abstract
Background: E-cigarettes are promoted for smoking cessation due to their having lower toxicity than cigarettes, but they are often used recreationally and linked to cardiovascular, respiratory, and mental health risks. Clinicians must understand usage patterns and influencing factors to guide patients in reducing or quitting their use. Methods: We surveyed consecutive cardiac inpatients admitted to the University of Ottawa Heart Institute between November 2019 and May 2020. Surveys were conducted in-person or via telephone. Descriptive statistics and logistic regression were used to examine factors associated with vaping status. Results: During the evaluation, 1616 cardiac patients were admitted and discharged; 124 (7.7%) were ineligible, and 403 (24.9%) refused or were unreachable. A total of 1089 (73.0%) completed the survey. Among them, 10.3% had ever vaped, and 5.5% were current vapers. Of ever-users, 66.1% used vaping to quit smoking. Adjusted analysis showed that younger age, tobacco co-use, secondhand exposure at home, and lower education levels were significantly associated with e-cigarette use. Conclusions: This evaluation found a low overall rate of e-cigarette use among cardiac inpatients. However, e-cigarette use was more common among younger patients, tobacco users, and those exposed to tobacco or e-cigarettes at home. These factors highlight the importance of advising patients to reduce their exposure to tobacco and e-cigarettes in their home and social environments, as doing so may improve their chances of quitting. Incorporating future prospective research in additional populations and settings would help support the generalizability of these results and assess their impact on clinical outcomes.
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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 | 0.000 |
| 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".