Epidemiological Evidence Associating Secondhand Smoke Exposure with Cardiovascular Disease
Bibliographic record
Abstract
The objective of this paper was to review the epidemiological literature examining the association between secondhand smoke (SHS) and cardiovascular disease (CVD). Specifically, we examined the various screening methods available in assessing smoking behaviour and quantifying nicotine absorption. Further, we considered the natural history of those exposed to SHS and the associated risk of CVD. We reviewed routine methods used to assess exposure to SHS; evaluated the utility of subjective screening questions regarding smoking behaviour and examined the efficacy of nicotine and cotinine biomarkers used to quantify SHS exposure in epidemiological and clinical-based research. Self-reporting is practical and cost-effective in identifying smoking behaviour patterns, but is subject to recall bias and underestimation of exposure, especially in the presence of children. Nicotine and cotinine biomarkers have proven valuable in quantifying tobacco smoke absorption and establishing biological plausibility. A combination of SHS self-reported and biomarker evaluation provide the most stringent method of establishing exposure. Sufficient evidence is reported in epidemiological research to support a causal association between SHS exposure and increased risks of CVD morbidity and mortality among both men and women. The risk of developing an acute cardiac syndrome or chronic lifetime coronary events is at least 30%. Similarly, reduction in the incidence of a myocardial infarction decreases by nearly 50% in the absence of SHS. Considering the biological plausibility and dose-response relationship between SHS and CVD, effective interventions that incorporate a comprehensive screening method of behavioral and biological measures of exposure coupled with efficacious treatment should elicit favorable change for at-risk populations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".