The association between smoking exposure and endothelial function evaluated using flow-mediated dilation values: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Tobacco use is recognized as a major cause of cardiovascular disease, which is associated with endothelial dysfunction. Endothelial function is evaluated using flow-mediated dilation (FMD), which is a noninvasive method. This meta-analysis aimed to investigate the association between smoking exposure and endothelial function evaluated using FMD values. METHODS: We searched the PubMed, Embase, Web of Science, and Cochrane Library databases for cohort studies of smokers or passive smokers that used FMD to assess endothelial function. The primary outcome of the study was the change in the rate of FMD. The risk of bias was evaluated using the Cochrane Collaboration tool and Newcastle-Ottawa Scale. Further, the weighted mean difference was used to analyze the continuous data. RESULTS: Overall, 14 of 1426 articles were included in this study. The results of these articles indicated that smoking is a major cause of endothelial dysfunction and altered FMD; a pooled effect size of - 3.15 was obtained with a 95% confidence interval of (- 3.84, - 2.46). Notably, pregnancy status, Asian ethnicity, or health status did not affect heterogeneity. CONCLUSIONS: We found that smoking has a significant negative impact on FMD, and measures such as medication or education for smoking cessation may improve endothelial function and reduce the risk of cardiovascular disease. TRIAL REGISTRATION: The meta-analysis was registered with PROSPERO on April 5th, 2023 (CRD42023414654).
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.057 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".