Bibliographic record
Abstract
Background and challenges to implementation Actual absorption dose of nicotine or tar from a cigarette could be different according to smokers' smoking topography. Different topographic characteristics, i. e., puff volume or puff frequency are applied to a standard operating procedure for intense simulation test of smoking in Canada, US, and ISO regimen. However, in South Korea, lack of smoking topography information limits estimation of intake dose of toxic or harmful chemicals from smoking and its health effect. In this study, we measured Korean smokers' topography and evaluated its characteristics. Intervention or response Under a convenient sampling design, we recruited 300 adult smokers (male:250, female: 50). Using CReSS pocket device (BORGWALDT, Richmond, VA 23237, USA), we obtained distributions of puff volume, puff duration, puff interval, etc. from their cigarettes smoked. For those who completed his/her topograph test, we collected urine samples for measurement of cotinine, OH-cotinine and NNAL using LCMSMS. Results and lessons learnt Smokers using cigarettes with higher amount of nicotine (HAN) (>0.1 mg) tend to have lower puff counts (15.0 (13.0 ~ 18.0) than those smokers (17.0 (15.0 ~ 21.0) smoked cigarettes with lower amount of nicotine (LAN) (≤0.1mg). Controlling for the number of cigarettes smoked, Korean smokers smoked with shorter inter puff interval than Whites. Total puff volume per day were similar between male and female smokers indicating the amount of toxic components inhaled from smoking might be similar between male and female smokers. Conclusions and key recommendations This study provides quantitative evidence that Korean smokers smoking cigarettes with shorter interval.
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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 | 0.000 | 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) | 0.002 | 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".