Can resveratrol in wine protect against the carcinogenicity of ethanol? A probabilistic dose‐response assessment
Bibliographic record
Abstract
Resveratrol, which may occur in wine, was suggested to act as a chemopreventive agent against the carcinogenic effects of ethanol. The assumption was based on data from experimental animals, which have shown that resveratrol above certain thresholds may reduce the incidence of tumours in several of the alcohol-related cancer sites (colon, liver and female breast). Using a probabilistic Monte Carlo type methodology, we estimated daily intake based on chemical analysis of resveratrol (n = 672) and ethanol (n = 867). Benchmark dose (BMD)-response modelling was conducted for resveratrol based on eight animal experiments, whereas BMD data for ethanol were taken from the literature. The margin of exposure (MOE) was calculated for both substances as an indicator if the intake may reach effective dosages. For intake of one 100-mL glass of wine, the average MOE was found to be 4.1 for ethanol and 459,937 for resveratrol. In the best-case scenario for resveratrol (e.g., very high contents and assuming a low effective dosage), the minimum MOE would be 111, which means that 111 glasses of wine need to be consumed daily to reach the BMD. The MOE ratio between resveratrol and ethanol is 166,128 on average, meaning that per glass of wine, ethanol is more than 100,000 times more potent than resveratrol. As resveratrol intake may not optimally reach the effective dosage, our study excludes a preventive effect of this substance on alcohol-related cancer. Commercial information about cancer-preventive or -protective effects of resveratrol in wine is misleading and must be prohibited.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".