Comparative risk assessment of tobacco smoke constituents using the margin of exposure approach: the neglected contribution of nicotine
Bibliographic record
Abstract
Nicotine was not included in previous efforts to identify the most important toxicants of tobacco smoke. A health risk assessment of nicotine for smokers of cigarettes was conducted using the margin of exposure (MOE) approach and results were compared to literature MOEs of various other tobacco toxicants. The MOE is defined as ratio between toxicological threshold (benchmark dose) and estimated human intake. Dose-response modelling of human and animal data was used to derive the benchmark dose. The MOE was calculated using probabilistic Monte Carlo simulations for daily cigarette smokers. Benchmark dose values ranged from 0.004 mg/kg bodyweight for symptoms of intoxication in children to 3 mg/kg bodyweight for mortality in animals; MOEs ranged from below 1 up to 7.6 indicating a considerable consumer risk. The dimension of the MOEs is similar to those of other tobacco toxicants with high concerns relating to adverse health effects such as acrolein or formaldehyde. Owing to the lack of toxicological data in particular relating to cancer, long term animal testing studies for nicotine are urgently necessary. There is immediate need of action concerning the risk of nicotine also with regard to electronic cigarettes and smokeless tobacco.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".