Exposure to Nicotine and Carcinogens among Southwestern Alaskan Native Cigarette Smokers and Smokeless Tobacco Users
Bibliographic record
Abstract
BACKGROUND: The prevalence of tobacco use, both cigarette smoking and smokeless, including iqmik (homemade smokeless tobacco prepared with dried tobacco leaves mixed with alkaline ash), and of tobacco-related cancer is high in Alaskan Native people (AN). To investigate possible mechanisms of increased cancer risk we studied levels of nicotine and tobacco-specific nitrosamines (TSNA) in tobacco products and biomarkers of tobacco toxicant exposure in Southwestern AN people. METHODS: Participants included 163 cigarette smokers, 76 commercial smokeless tobacco, 20 iqmik, 31 dual cigarette smokers and smokeless tobacco, and 110 nontobacco users. Tobacco use history, samples of tobacco products used, and blood and urine samples were collected. RESULTS: Nicotine concentrations were highest in cigarette tobacco and TSNAs highest in commercial smokeless tobacco products. The AN participants smoked on average 7.8 cigarettes per day. Nicotine exposure, assessed by several biomarker measures, was highest in iqmik users, and similar in smokeless tobacco and cigarette smokers. TSNA exposure was highest in smokeless tobacco users, and polycyclic aromatic hydrocarbon exposure was highest in cigarette smokers. CONCLUSIONS: Despite smoking fewer cigarettes per day, AN cigarette smokers had similar daily intake of nicotine compared to the general U.S. population. Nicotine exposure was greatest from iqmik, likely related to its high pH due to preparation with ash, suggesting high addiction potential compared to other smokeless tobacco products. TSNA exposure was much higher with smokeless tobacco than other product use, possibly contributing to the high rates of oral cancer. IMPACT: Our data contribute to an understanding of the high addiction risk of iqmik use and of the cancer-causing potential of various forms of tobacco use among AN people.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".