Bibliographic record
Abstract
Objective Oral tobacco products contain nicotine and carcinogenic tobacco-specific N-nitrosamines (TSNAs) that can be absorbed through the oral mucosa. The aim of this study was to determine typical pH ranges and concentrations of total nicotine, unionised nicotine (the most readily absorbed form) and five TSNAs in selected oral tobacco products distributed globally. Methods A total of 53 oral tobacco products from 5 World Health Organisation (WHO) regions were analysed for total nicotine and TSNAs, including 4-(methyl-nitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), using gas chromatography or liquid chromatography with mass spectrometric detection. Unionised nicotine concentrations were calculated using product pH and total nicotine concentrations. Fourier transform infrared spectroscopy was used to help categorise or characterise some products. Results Total nicotine content varied from 0.16 to 34.1 mg/g product, whereas, the calculated unionised nicotine ranged from 0.05 to 31.0 mg/g product; a 620-fold range of variation. Products ranged from pH 5.2 to 10.1, which translates to 0.2% to 99.1% of nicotine being in the unionised form. Some products have very high pH and correspondingly high unionised nicotine (eg, gul powder, chimó, toombak) and/or high TSNA (eg, toombak, zarda, khaini) concentrations. The concentrations of TSNAs spanned five orders of magnitude with concentrations of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) ranging from 4.5 to 516 000 ng/g product. Conclusions These data have important implications for risk assessment because they show that very different exposure risks may be posed through the use of these chemically diverse oral tobacco products. Because of the wide chemical variation, oral tobacco products should not be categorised together when considering the public health implications of their use.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.674 | 0.518 |
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".