Abstract WP185: Relationship Between E-cigarette Smoking And Stroke - A NHANES Study
Bibliographic record
Abstract
Introduction: Tobacco use is a known risk factor for stroke and other cardiovascular diseases. However, the relationship between e-cigarette smoking and stroke is largely unknown. Hypothesis: This study aims to assess the effect of e-cigarettes among people who have had a history of stroke. Methods: A cross-sectional survey was performed using the NHANES database from 2015 to 2018 in the US population. We identified participants 18 or older with a history of stroke and their smoking habits (e-cigarette, traditional, and dual smoking). The chi-square test, unpaired t-test, and multivariable logistic regression models were used to examine the association of e-cigarette consumption among the stroke population. Results: Out of a total of 266,058 respondents, 79,825 were smokers [E-cigarette: 7,756 (9.72%); Traditional: 48,625(60.91%), Dual smokers: 23,444 (29.37%)] Overall prevalence of stroke was 5.41% (n=4194) amongst various types of smokers. Amongst females with stroke, the prevalence of e-cigarette use was higher compared to traditional smoking. (36.36% vs 33.91%; p<0.001) Mexican Americans (21.21% vs. 6.02%) and other Hispanics (24.24% vs. 7.70%) had a higher prevalence of e-cigarette use than traditional smoking. (p<0.001) E-cigarette smokers were having an early onset of stroke in comparison with dual and traditional smokers. (Median age in years: 48 vs. 50 vs. 59, respectively; p<0.001). Stroke was more prevalent amongst traditional smokers compared to e-cigarette and dual smokers (6.75% vs. 1.09% vs. 3.72% p<0.001). In multivariable logistic regression analysis, the odds of having a history of stroke were higher amongst e-cigarettes in comparison with traditional smokers. (aOR:1.15; 95%CI: 1.15-1.16; p<0.001). Conclusion: Although stroke was more prevalent in traditional smokers, e-cigarette smokers had early-onset and higher odds of stroke compared to traditional smokers. More prospective studies are needed to evaluate the long-term effects and safety of e-cigarettes to mitigate the risk of cardio and cerebrovascular disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".