Biomarkers of Exposure to Nicotine and Selected Toxicants in Individuals Who Use Alternative Tobacco Products Sold in Japan and Canada from 2018 to 2019
Bibliographic record
Abstract
BACKGROUND: Comparisons of nicotine and toxicant exposures between people who use different alternative tobacco products remain underexplored. METHODS: This cross-sectional, multicountry study analyzed urinary metabolites of nicotine, tobacco-specific nitrosamines [4-methylnitrosamino-1-3-pyridyl-1-butanone (NNK)], and volatile organic compounds (acrolein, acrylamide, and acrylonitrile) among established users (n = 550) in Japan and Canada. Participants exclusively or concurrently used nicotine vaping products (NVP; Canada only), heated tobacco products (HTP; Japan only), and combustible cigarettes (CC; Japan and Canada) or abstained (Japan and Canada). RESULTS: All product groups showed substantial nicotine exposure. Both HTPs and NVPs exposed exclusive users to lower toxicant levels than exclusive CC use. Canadian participants who exclusively used NVPs exhibited lower NNK and acrolein exposure but higher acrylamide exposure than Japanese participants who exclusively used HTPs. Concurrent use of CCs alongside alternative products exposed users to higher toxicant levels compared with exclusive use of either alternative product. CONCLUSIONS: Exclusive use of alternative tobacco products results in significant nicotine exposure but substantially lower toxicant exposure compared with exclusive CC use. People who use HTPs in Japan may experience higher exposure to nicotine and certain toxicants (NNK and acrolein) than people who use NVPs in Canada. Concurrent use results suggest that partially substituting CCs with alternative products may reduce toxicant exposure but to a lesser extent than completely transitioning to alternative products. IMPACT: Exposure patterns between two popular alternative tobacco products differ. The overall toxicant exposure from these products is lower than from CCs, providing critical data for regulatory decisions and public health considerations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".