Smoking topography and biomarkers of exposure among Japanese smokers: associations with cigarette emissions obtained using machine smoking protocols
Bibliographic record
Abstract
OBJECTIVES: Although the relative risk of lung cancer due to smoking is reported to be lower in Japan than in other countries, few studies have examined the characteristics of Japanese cigarettes or potential differences in smoking patterns among Japanese smokers. METHODS: To examine tar, nicotine and carbon monoxide (TNCO) emissions from ten leading cigarettes in Japan, machine smoking tests were conducted using the International Organization for Standardization (ISO) protocol and the Health Canada Intense (HCI) protocol. Smoking topography and tobacco-related biomarkers were collected from 101 Japanese smokers to examine measures of exposure. RESULTS: The findings indicate considerable variability in the smoking behavior of Japanese smokers. On average, puffing behaviors observed among smokers were more similar to the parameters of the HCI protocol, and brands with greater ventilation that yielded lower machine values using the ISO protocol were smoked more intensely than brands with lower levels of ventilation. The smokers of "ultra-low/low" nicotine-yield cigarettes smoked 2.7-fold more intensively than those of "medium/high" nicotine-yield cigarette smokers to achieve the same level of salivary cotinine (p = 0.024). CO levels in expiratory breath samples were associated with puff volume and self-reported smoking intensity, but not with nominal values of nicotine-yield reported on cigarette packages. CONCLUSIONS: Japanese smokers engaged in "compensatory smoking" to achieve their desired nicotine intake, and levels of exposure were greater than those suggested by the nominal value of nicotine and tar yields reported on cigarette packages.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".