Tobacco Smoke–Related Health Effects Induced by 1,3-Butadiene and Strategies for Risk Reduction
Bibliographic record
Abstract
1,3-Butadiene (BD) is a smoke component selected by the World Health Organization (WHO) study group on Tobacco Product Regulation (TobReg) for mandated lowering. We examined the tobacco smoke-related health effects induced by BD and possible health impacts of risk reduction strategies. BD levels in mainstream smoke (MSS) from international and Canadian cigarettes and environmental tobacco smoke (ETS) were derived from scientific journals and international government reports. Dose-response analyses from toxicity studies from government reports were evaluated and the most sensitive cancer and noncancer endpoints were selected. The risks were evaluated by taking the ratio (margin of exposure, MOE) from the most sensitive toxicity endpoint and appropriate exposure estimates for BD in MSS and ETS. BD is a good choice for lowering given that MSS and ETS were at levels for cancer (leukemia) and noncancer (ovarian atrophy) risks, and the risks can be significantly lowered when lowering the BD concentrations in smoke. Several risk reduction strategies were analyzed including a maximum level of 125% of the median BD value per milligram nicotine obtained from international brands as recommended by the WHO TobReg, tobacco substitute sheets, dual and triple carbon filters, and polymer-derived carbon. The use of tobacco substitute sheet with a polymer-derived carbon filter resulted in the most significant change in risk for cancer and noncancer effects. Our results demonstrate that MOE analysis might be a practical way to assess the impact of risk reduction strategies on human health in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".