Smoking Topography, Brand Switching, and Nicotine Delivery: Results from an <i>In vivo</i> Study
Bibliographic record
Abstract
OBJECTIVE: Exposure to toxins in tobacco smoke is influenced by how a cigarette is smoked. Cigarettes have been designed to allow for a range of puffing behavior and to provide different, nonlinear tar and nicotine yields in response to different puffing profiles. However, puffing behavior and its influence upon risk-exposure has yet to be assessed outside the laboratory, in smokers' natural environment. METHOD: Fifty-nine adult smokers used a portable device to measure smoking topography over the course of three 1-week trials. Participants were asked to smoke their usual "regular yield" brand through the device for trial 1 and again, 6 weeks later, at trial 2. Half the subjects were then randomly assigned to switch to a "low-yield" brand for trial 3. RESULTS: The findings show a high degree of stability in puffing behavior within the same subject over time but considerable variability between smokers. Smokers who were switched to a "low-yield" cigarette increased their total smoke intake per cigarette by 40% (P = 0.007), with no significant change in their salivary cotinine levels. Cigarettes smoked per day and nicotine yield were only weakly associated with salivary cotinine levels; however, salivary cotinine was strongly associated with a composite measure that included cigarettes per day, brand elasticity, and puffing behavior (sr = 0.61, P < 0.001). CONCLUSIONS: These findings provide strong evidence of behavioral compensation to low-yield cigarettes from in vivo measures of smoking behavior. The findings also show the importance of brand elasticity and smoking topography in predicting nicotine uptake and smoke exposure.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".