Associations of cigarette smoking and burden of thoracic aortic calcification in asymptomatic individuals: A dose-response relationship
Bibliographic record
Abstract
Smoking is known as a powerful predictor of pathological coronary atherosclerosis. Thoracic aortic calcification (TAC), an alternative marker for pathological atherosclerosis, has also been shown to be associated unfavorable cardiovascular outcomes. We aimed to investigate the dose-response relationship between cigarette use and calcification burden in subjects free from clinical symptoms. Among 3109 patients enrolled in this analysis, we categorized study participants according to smoking exposure pattern as: non-smokers, ex-smokers and current smokers. Smoking dose (cigarette/day), duration (years) and pack-years were semi-quantified as smoking dose exposure variables. Thoracic aortic calcification burden (including TAC score, plaque volume and plaque density) were determined and related to smoking dose and pattern information. TAC burdens (including TAC score, plaque volume and density) were highest in current smoker compared to non-smoker group, with ex-smoker showing TAC burdens in-between (all ANOVA p<0.05). Linear regression models consistently demonstrated that TAC burdens as continuous variables were independently higher in a dose-dependent manner with smoking exposure, particularly in high-dose (> 10 cigarettes/day) and the long-duration (> 3 years) smokers, even after adjusting for baseline demographic differences (all p<0.05). By logistic regression, subjects who never smoke consistently demonstrated reduced risk of TAC existence (adjusted OR: 0.65 [95% CI: 0.48-0.86], P = 0.003) in contrary to those current smokers (adjusted OR: 1.47 [95% CI: 1.10-1.89], P = 0.009). A dose-response relationship between active cigarette use and TAC burden was observed, with those who never exposed to smoking or quitted demonstrating partial protective effects. Our data provided imaging-based evidence about the potential deleterious biological hazards of long-term and high-dose cigarette consumption.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".