Biomarkers of Exposure Among “Dual Users” of Tobacco Cigarettes and Electronic Cigarettes in Canada
Bibliographic record
Abstract
INTRODUCTION: "Dual use" refers to the concurrent use of tobacco cigarettes (smoking) and electronic cigarettes (e-cigarettes; vaping). Although dual use is common among e-cigarette users, there is little evidence regarding biomarkers of exposure among dual users and how these change under different conditions of product use. METHODS: A nonblinded within-subjects crossover experiment was conducted with adult daily dual users (n = 48) in Ontario, Canada. Participants completed three consecutive 7-day periods in which the use of tobacco cigarettes and e-cigarettes was experimentally manipulated, resulting in four study conditions: Dual use, Tobacco cigarette use, E-cigarette use, and No product use. Repeated measures models were used to examine changes in product use and biomarkers of exposure. RESULTS: Compared to dual use, cotinine remained stable when participants exclusively smoked (p = .524), but significantly decreased when they exclusively vaped (p = .027), despite significant increases in e-cigarette consumption (p = .001). Levels of biomarkers of exposure to toxicants, including carbon monoxide (CO), 1-hydroxypyrene (1-HOP), and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), were significantly lower when participants exclusively vaped than when they engaged in dual use (CO = -41%, p < .001; 1-HOP = -31%, p = .025; NNAL = -30%, p = .017). Similar findings were observed among participants abstaining from both products as compared to dual use (CO: -26%, p < .001; 1-HOP = -14% [ns]; NNAL = -35%, p = .016). In contrast, levels of biomarkers of exposure increased when participants exclusively smoked as compared to dual use (CO = +21%, p = .029; 1-HOP = +23%, p = .048; NNAL = +8% [ns]). CONCLUSIONS: Although dual use may reduce exposure to tobacco smoke constituents to some extent, abstaining from smoking is the most effective way to reduce such exposure. IMPLICATIONS: Public health authorities should clearly communicate the relative risk of e-cigarettes and tobacco cigarettes to the general public, focusing on two salient points: (1) e-cigarettes are not harmless, but they are less harmful than tobacco cigarettes; and (2) using e-cigarettes while smoking may not necessarily reduce health risks; therefore, consumers should stop smoking completely to maximize potential health benefits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".