Scope for regulation of cigarette smoke toxicity according to brand differences in published toxicant emissions.
Bibliographic record
Abstract
AIMS: To explore the scope for regulating to reduce the toxicity of manufactured cigarettes sold in New Zealand (NZ), based on published toxicant emissions by brand. METHODS: Internet searches of published cigarette smoke emissions of 13 toxicants chosen on risk assessment principles, for 20 British Columbian, 15 Australian brands, and one NZ brand, Holiday Extra-mild (HEM), tested by Health Canada intensive smoke machine method at Labstat Inc, Kitchener, Ontario, as a ratio of toxicant to nicotine yield. We estimated relative overall smoke toxicity per disease group and per brand, after adjusting for the published cigarette-attributed mortality fractions for cancer, cardiovascular, and respiratory disease. RESULTS: After allowing for nicotine yield, filter ventilation, and compensatory over-smoking, there were significant differences between brands, with the NZ brand estimated to be the most toxic. Low-yield cigarettes (<0.9 mg nicotine ISO) were estimated to be on average 19% more potent overall than medium-yield cigarettes (p<0.01). Of toxicants identified and measurable in smoke; 1,3-butadiene accounted for 45% of cancer potency; hydrogen cyanide for 89% of cardiovascular; and acrolein for 97% of respiratory potency--these three toxicants accounting for 65% of identified brand potency. Individual toxicant emissions varied across brands by a factor of 1.5 for carbon monoxide, to 32 for lead. Compared with HEM, one Canadian brand, 'Export A full flavor', carried a 37% lower cancer risk. This lower risk was largely due to differences in nicotine yield, lowering the toxicant/nicotine ratio. CONCLUSIONS: Cigarettes, unregulated, are unduly dangerous. Though many smoke toxicants cannot yet be quantified, risk assessment based on current data suggests that regulation could partly reduce identifiable cancer risk, and possibly eliminate the excess cardiovascular and respiratory toxicity of HEM, when compared with regular Canadian brands. The first goal should be to reduce emissions of the leading three toxicants, in addition to more effective charcoal filters. Tobacco smoke, unlike unburnt or non-smoking tobacco, contains toxic gases and trillions of reactive oxygen species molecules per puff, and will remain inherently harmful. Regulation could usefully part-reduce smoke toxicity exposure for continuing smokers, while not relenting on efforts to assist smokers and society to be quit of smoking.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".