Concentrations of nicotine, nitrosamines, and humectants in legal and illegal cigarettes in Mexico
Bibliographic record
Abstract
BACKGROUND: Article 10 of the World Health Organization Framework Convention on Tobacco Control states the need for industry disclosure of tobacco contents and emissions. Currently, the profiles of key tobacco compounds in legal and illegal cigarettes are largely unknown. We aimed to analyze and compare concentrations of nicotine, nitrosamines, and humectants in legal and illegal cigarettes collected from a representative sample of smokers. METHODS: Participants of the International Tobacco Control cohort provided a cigarette pack of the brand they smoked during the 2014 wave. Brands were classified as legal or illegal according to the Mexican legislation. Nicotine, nitrosamines, glycerol, propylene glycol, and pH were quantified in seven randomly selected packs of each brand. All analyses were done blinded to legality status. Average concentrations per brand and global averages for legal and illegal brands were calculated. Comparisons between legal and illegal brands were conducted using t tests. RESULTS: Participants provided 76 different brands, from which 6.8% were illegal. Legal brands had higher nicotine (15.05 ± 1.89 mg/g vs 12.09 ± 2.69 mg/g; p < 0001), glycerol (12.98 ± 8.03 vs 2.93 ± 1.96 mg/g; p < 0.001), and N-nitrosanatabine (NAT) (1087.5 ± 127.0 vs 738.5 ± 338 ng/g; p = 0.006) concentrations compared to illegal brands. For all other compounds, legal and illegal brands had similar concentrations. CONCLUSION: Compared to illegal cigarettes, legal brands seem to have higher concentrations of nicotine, NAT, and glycerol. Efforts must be made to implement and enforce Article 10 of the Framework Convention on Tobacco Control to provide transparent information to consumers, regulators, and policy-makers; and to limit cigarette engineering from the tobacco industry.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".