Comparative risk assessment of carcinogens in alcoholic beverages using the margin of exposure approach
Bibliographic record
Abstract
Alcoholic beverages have been classified as carcinogenic to humans. As alcoholic beverages are multicomponent mixtures containing several carcinogenic compounds, a quantitative approach is necessary to compare the risks. Fifteen known and suspected human carcinogens (acetaldehyde, acrylamide, aflatoxins, arsenic, benzene, cadmium, ethanol, ethyl carbamate, formaldehyde, furan, lead, 4-methylimidazole, N-nitrosodimethylamine, ochratoxin A and safrole) occurring in alcoholic beverages were identified based on monograph reviews by the International Agency for Research on Cancer. The margin of exposure (MOE) approach was used for comparative risk assessment. MOE compares a toxicological threshold with the exposure. MOEs above 10,000 are judged as low priority for risk management action. MOEs were calculated for different drinking scenarios (low risk and heavy drinking) and different levels of contamination for four beverage groups (beer, wine, spirits and unrecorded alcohol). The lowest MOEs were found for ethanol (3.1 for low risk and 0.8 for heavy drinking). Inorganic lead and arsenic have average MOEs between 10 and 300, followed by acetaldehyde, cadmium and ethyl carbamate between 1,000 and 10,000. All other compounds had average MOEs above 10,000 independent of beverage type. Ethanol was identified as the most important carcinogen in alcoholic beverages, with clear dose response. Some other compounds (lead, arsenic, ethyl carbamate, acetaldehyde) may pose risks below thresholds normally tolerated for food contaminants, but from a cost-effectiveness point of view, the focus should be on reducing alcohol consumption in general rather than on mitigative measures for some contaminants that contribute only to a limited extent (if at all) to the total health risk.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".