Epidemiology-based risk assessment using the benchmark dose/margin of exposure approach: the example of ethanol and liver cirrhosis
Bibliographic record
Abstract
BACKGROUND: A novel approach to derive a threshold dose with respect to alcohol-related harm, the benchmark dose (BMD) methodology, is introduced to provide a basis for evidence-based drinking guidelines. This study is the first to calculate a BMD for alcohol exposure using epidemiological cohort data. With this BMD we will be able to calculate the margin of exposure (MOE) for alcohol consumption, which can be used for comparative risk assessment and applied to setting public health policy. METHODS: Benchmark dose-response modelling of epidemiological data gathered during a recent systematic review and meta-analysis of alcohol consumption as a risk factor for liver cirrhosis morbidity and mortality. RESULTS: For a benchmark response (BMR) of 1.5%, the resulting BMD values were 30.9 g/day for males and 29.7 g/day for females; the corresponding lower one-sided confidence values were 25.7 and 27.2 g/day, respectively. The intake scenario for the Canadian population resulted in an MOE of 1.23. Intake scenarios for individuals as based on the Canadian drinking guidelines led to MOE values between 0.96 and 1.91. Using an uncertainty factor of 10, the acceptable daily intake for alcohol would be 2.6 g/day. CONCLUSIONS: The BMD approach was feasible in developing evidence-based guidelines for low-risk drinking. As our calculated MOEs result around unity (i.e. 1) for moderate drinking, it is evident that the current guidelines correspond very well to low risk on the dose-response curve. The BMD methodology therefore validates current guidelines. The results again highlight the health risk associated with alcohol consumption.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".