Effect of Tobacco Product Types on Cardiovascular Disease Risk Factors
Bibliographic record
Abstract
Tobacco use is one of the key contributors to cardiovascular disease (CVD), while hypertension, high cholesterol, and diabetes are three traditional CVD risk factors. With the diversification of tobacco products and different usage patterns among age groups, the specific influence of smoking on CVD risk factors remains unclear. Therefore, a comprehensive understanding of these effects is crucial for enhancing public health awareness. This study applies logistic regression and age stratification to uncover the link between tobacco product types and CVD risk factors by using 2015-2018 NHANES data. Our study demonstrates that different types of tobacco use increase the chance of CVD risk factors, and the impact varies by product type and age group. The research focuses on individuals aged above 50 and identifies that hypertension risk is higher among current smokeless tobacco users. Former cigarette users and former cigar users have a higher chance of having high cholesterol and diabetes. Current cigarette use is linked to lower diabetes risk. This work improves the knowledge of the influence of tobacco use on CVD risk factors among the elderly, who are more vulnerable to health issues, providing valuable insights for public health initiatives and tobacco control policies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".