TSNA Exposure: Levels of NNAL Among Canadian Tobacco Users
Bibliographic record
Abstract
INTRODUCTION: Tobacco smoke contains more than 7,000 chemicals, including known carcinogens, such as tobacco-specific nitrosamines (TSNAs). TSNA levels in cigarettes vary considerably within and across markets; however, the extent to which these different TSNA levels translate into differences in human exposure and risk remains unclear. The current study sought to examine TSNA exposure among Canadian tobacco users. METHODS: Data from the 2007-2009 Canadian Health Measures Survey were used to measure levels of urinary NNAL [4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol], a metabolite of the TSNA NNK [4-(methylnitrosamino-1-(3-pyridyl)-1-butanone], among tobacco users (n = 507). Geometric mean concentrations of total urinary NNAL and creatinine-corrected total urinary NNAL were calculated. A linear regression model was used to examine predictors of urinary levels of NNAL. RESULTS: The mean population level of total urinary NNAL and creatinine-corrected total urinary NNAL was 71.2 pg/ml and 82.0 pg/mg creatinine, respectively. NNAL levels were higher among older respondents (p = .02), among females (p = .04), and among those with greater daily cigarette consumption (p < .0001), greater levels of urinary free cotinine (p < .0001), and greater levels of urinary creatinine (p < .0001). Overall, the mean level of urinary total NNAL among Canadian tobacco users was approximately one fourth that of their U.S. counterparts. CONCLUSIONS: The study findings provide the first nationally representative characterization of TSNA exposure among Canadian tobacco users. Although the findings indicate marked differences in TSNA exposure between Canadian and American populations of tobacco users, it is not known whether these differences in exposure translate into differences in risk.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".