Mouth level smoke exposure using analysis of filters from smoked cigarettes: A study of eight countries
Bibliographic record
Abstract
The analysis of spent cigarette filters enables the estimation of the nicotine and tar (nicotine-free dry particulate matter) yields obtained by smokers in their everyday environment and has been shown to correlate well with biomarkers of exposure. Leading products across the range of ISO tar yields were selected from Australia, Brazil, Canada, Germany, Japan, New Zealand, South Africa and Switzerland. At least fifty demographically representative smokers were recruited per product. Subjects, ≥ 21 years of age and smoking ≥ 5 cigarettes per day, were asked to collect ≥ 15 filters from cigarettes they had smoked. The collected filters were analysed for nicotine and UV absorbance to enable the smokers' mouth level exposure to nicotine and tar to be estimated and a comparison of countries and tobacco blend styles to be made. Smoking history data were also collected. More than 80,000 filters were collected from 5703 smokers of 106 products from eight countries. Mean ± SD estimated nicotine exposures per cigarette and per day ranged from 0.93 ± 0.34 mg/cigarette (Brazil) to 1.77 ± 0.69 mg/cigarette (South Africa) and from 16.4 ± 11.1mg/day (Germany) to 31.5 ± 14.8 mg/day (South Africa), respectively. Male smokers obtained higher mean estimated tar and nicotine exposures than female smokers. These gender differences were statistically significant for six countries. Significant correlations were found between estimated nicotine exposure and ISO nicotine yield, and between estimated tar exposure and ISO tar yield (p<0.001).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".