Vaping is associated with increased length of stay among cardiac inpatients
Bibliographic record
Abstract
INTRODUCTION: The rapid increase in e-cigarette use, especially among youth, raises significant health concerns. Understanding their impact on high-risk populations, such as those with cardiovascular disease, is crucial for improving patient outcomes and reducing healthcare utilization. The aim of this study is to assess the impact of e-cigarette use on hospital length of stay (LOS) in patients with cardiovascular disease. METHODS: This cross-sectional survey was conducted at the University of Ottawa Heart Institute (November 2019-May 2020) among consecutive cardiology inpatients. Eligible participants were those admitted to the cardiac unit, fluent in French or English, and without cognitive or hearing impairments. The primary outcome is length of hospital stay. Data analysis included descriptive statistics and adjusted linear regression to explore e-cigarette use and hospital stay length, with significance set at p<0.05. RESULTS: Of 1616 cardiac patients, 1089 (73.0%) completed the survey. E-cigarette ever users were 10.4% (4.9% former, 5.5% current). Mean LOS was 11.03 days, longer for ever-users (13.1 days) than never-users (10.8 days). Ever users had a significantly longer LOS by 2.45 days (p=0.040), and current users by 3.24 days (p=0.039). CONCLUSIONS: E-cigarette use is associated with longer hospital stays among cardiac patients, even after adjusting for confounders. This underscores the potential harmful effects of vaping on cardiac recovery. Further research is needed to explore these associations and their implications for healthcare utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".