RETRACTED: Effect Comparison of E-Cigarette and Traditional Smoking and Association with Stroke—A Cross-Sectional Study of NHANES
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Introduction: Tobacco use is one of the most significant risk factors for stroke. Besides traditional cigarettes and combustible products, the use of e-cigarettes and electronic nicotine delivery products has been widespread among young adults in the recent era. Furthermore, the trend of vaping has increased over the last decade. However, the relationship between e-cigarettes and stroke is largely unknown. The aim of this study was to evaluate the prevalence and identify the relationship between e-cigarette smoking and stroke. Methods: A cross-sectional study was performed using the NHANES database of the US population. Adults with a history of smoking were considered in our study and divided into three groups, e-cigarette users, traditional, and dual smokers. The Chi-squared test, Wilcoxon rank-sum test, and multivariable logistic regression analysis were used to identify the prevalence and association of e-cigarette consumption and stroke. Results: Out of a total of 266,058 respondents from 2015 to 2018, we found 79,825 respondents who smoked e-cigarettes (9.72%) or traditional (29.37%) or dual smoking (60.91%). Stroke prevalence among e-cigarette smokers was 1.57%. Stroke was more prevalent among traditional smokers than among e-cigarette smokers. (6.75% vs. 1.09%; p < 0.0001) E-cigarette smokers had early onset of stroke in comparison with traditional smokers. (median age: 48 vs. 59 years; p < 0.0001). Among females with stroke, the prevalence of e-cigarette use was higher in comparison with traditional smoking (36.36% vs. 33.91%; p < 0.0001). Among the stroke population, the prevalence of e-cigarette use was higher among Mexican-Americans (21.21% vs. 6.02%) and other Hispanics (24.24% vs. 7.70%) compared with traditional smoking (p < 0.0001). The regression analysis found higher odds of stroke history among e-cigarette users than traditional smokers [aOR: 1.15; 95% CI: 1.15−1.16)]. Conclusion: Though stroke was more prevalent in traditional smokers, the incidence of stroke was early-in-onset and was strongly associated with e-cigarette use compared to traditional smokers. We have also identified vascular effects of e-cigarettes components as possible triggers for the stroke.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".