Cigarette Yields and Human Exposure: A Comparison of Alternative Testing Regimens
Bibliographic record
Abstract
OBJECTIVE: There is general agreement that the testing protocol for measuring cigarette smoke constituents-the International Organization for Standardization regimen-is an inappropriate mechanism for evaluating human exposure. Alternative smoking regimens have been introduced in Canada and Massachusetts; however, these regimens have not been evaluated against human smoking behavior and biomeasures of exposure. The objective of this study was to compare measures of smoke volume and nicotine uptake among human smokers against the puffing variables and nicotine yields generated by five different machine smoking regimens: (a) International Organization for Standardization, (b) Massachusetts, (c) Canadian, (d) a Compensatory regimen, and (e) a Human Mimic regimen. METHODS: Measures of smoke volume and puffing behavior were recorded for 51 smokers who used a portable smoking topography device for three 1-week trials. Measures of salivary cotinine were taken at the completion of each week. The cigarette brands smoked by participants were then machine-smoked under five testing regimens, including a human mimic condition where brands were machine smoked using the puffing behavior recorded from human smokers. The total volume of smoke collected from each cigarette and the nicotine, tar, and carbon monoxide yields were recorded. RESULTS: None of the four machine smoking regimens adequately reflected Human Mimic Yields of tar, nicotine, and carbon monoxide. In addition, none of the four smoking regimens generated nicotine yields that were associated with actual nicotine uptake in humans. CONCLUSIONS: None of the existing smoking regimens adequately represents human smoking behavior nor do they generate yields associated with human measures of nicotine uptake.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".