Effect of Tobacco Product Types on Cardiovascular Disease Risk Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it