Application of toxicological risk assessment principles to the chemical constituents of cigarette smoke
Bibliographic record
Abstract
OBJECTIVE: To provide a hazard prioritisation for reported chemical constituents of cigarette smoke using toxicological risk assessment principles and assumptions. The purpose is to inform prevention efforts using harm reduction. DATA SOURCES: International Agency for Research on Cancer Monographs; California and US Environmental Protection Agency cancer potency factors (CPFs) and reference exposure levels; scientific journals and government reports from the USA, Canada, and New Zealand. STUDY SELECTION: This was an inclusive review of studies reporting yields of cigarette smoke constituents using standard ISO methods. DATA EXTRACTION: Where possible, the midpoint of reported ranges of yields was used. DATA SYNTHESIS: Data on 158 compounds in cigarette smoke were found. Of these, 45 were known or suspected human carcinogens. Cancer potency factors were available for 40 of these compounds and reference exposure levels (RELs) for non-cancer effects were found for 17. A cancer risk index (CRI) was calculated by multiplying yield levels with CPFs. A non-cancer risk index (NCRI) was calculated by dividing yield levels with RELs. Gas phase constituents dominate both CRI and NCRI for cigarette smoke. The contribution of 1,3-butadiene (BDE) to CRI was more than twice that of the next highest contributing carcinogen (acrylonitrile) using potencies from the State of California EPA. Using those potencies from the USEPA, BDE ranked third behind arsenic and acetaldehyde. A comparison of CRI estimates with estimates of smoking related cancer deaths in the USA showed that the CRI underestimates the observed cancer rates by about fivefold using ISO yields in the exposure estimate. CONCLUSIONS: The application of toxicological risk assessment methods to cigarette smoke provides a plausible and objective framework for the prioritisation of carcinogens and other toxicant hazards in cigarette smoke. However, this framework does not enable the prediction of actual cancer risk for a number of reasons that are discussed. Further, the lack of toxicology data on cardiovascular end points for specific chemicals makes the use of this framework less useful for cardiovascular toxicity. The bases for these priorities need to be constantly re-evaluated as new toxicology information emerges.
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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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".