Association of Smoking and E-Cigarette in Chronic Liver Disease: An NHANES Study
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
Background: There is an increased trend of e-cigarette but the toxic effects of e-cigarette metabolites are not widely studied especially in liver disease. Hence, we aimed to evaluate the prevalence and patterns of recent e-cigarette use in a nationally representative sample of US adults and adolescents and its association amongst respondents with liver disease. Methods: We conducted a retrospective cross-sectional study using National Health and Nutrition Examination Survey (NHANES) database from 2015 to 2018. The self-reported NHANES questionnaire was used to assess liver disease (MCQ160L, MCQ170L and MCQ 510 (a-e)), e-cigarette use (SMQ900) and traditional smoking status (SMQ020 or SMQ040). We conducted univariate analysis and multivariable logistic regression models to predict the association of e-cigarette use, traditional smoking and dual smoking amongst the population with liver disease. Results: Out of total 178,300 respondents, 7,756 (4.35%) were e-cigarette users, 48,625 (27.27%) traditional smoking, 23,444 (13.15%) dual smoking and 98,475 (55.23%) non-smokers. Females had a higher frequency of e-cigarette use (49.3%) compared to dual (43%) and traditional smoking (40.8%) (P < 0.0001). Respondents with a past history of any liver disease have lower frequency of e-cigarette use compared to dual and traditional smoking, respectively (2.4% vs. 6.4% vs. 7.2%; P < 0.0001). In multivariate logistic regression models, we found that e-cigarette users (odds ratio (OR): 1.06; 95% confidence interval (CI): 1.05 - 1.06; P < 0.0001) and dual smoking (OR: 1.50; 95% CI: 1.50 - 1.51; P < 0.0001) were associated with higher odds of having history of liver disease compared to non-smokers. Conclusion: Our study found that despite the low frequency of e-cigarette use in respondents with liver disease, there was higher odds of e-cigarette use amongst patients with liver disease. This warrants the need for more future prospective studies to evaluate the long-term effects and precise mechanisms of e-cigarette toxicants on the liver.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".