Smoking Cessation and Benefits to Cardiovascular Health: A Review of Literature
Bibliographic record
Abstract
Tobacco smoking is a chief cause of preventable deaths worldwide, accounting for various cancers, cardiovascular and respiratory diseases. Tobacco smoking accounts for more than seven million deaths every year. Worldwide statistics show that about 1.1 billion active smokers exist; 80% live in low- and middle-income countries. Nicotine is the addictive ingredient with the least harm compared to other active ingredients in tobacco, albeit not completely benign. Nicotine acts on the nicotinic cholinergic receptors (nAChRs) and produces the release of neurotransmitters. The mechanism by which it affects the cardiovascular system involves endothelial dysfunction by reducing nitrogen monoxide production, pro-thrombotic conditions, and activating inflammatory routes. These factors, along with the increased amounts of coronary atherosclerosis, have addictive adverse effects. Smoking has been shown to cause increased amounts of coronary atherosclerosis which may be responsible for the increased risk of hypertension, coronary heart disease, and atrial fibrillation, potentially contributing to the association of current smokers with a higher incidence of heart failure. This has led to worsened burdens and outcomes of cardiovascular disease among smokers. Smoking cessation has been associated with a reduction in cardiovascular mortality. This ranges from the reduction in the incidence of hypertension, type 2 diabetes, and heart failure. As regards behavioral and mental health, smoking cessation reduces the risk of cardiovascular disease in people experiencing mental illness. The prevalence of smoking continues to trend downward over the past couple of decades. Despite this downtrend, cigarette smoking is responsible for approximately half a million deaths per year in the United States and billions of dollars spent in healthcare. This buttresses the need to explore the various effects of smoking cessation on cardiovascular health and suggest ways to curb the disease burden.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".