Lifetime-risk of alcohol-attributable mortality based on different levels of alcohol consumption in seven European countries : Implications for low-risk drinking guidelines
Bibliographic record
Abstract
Low-risk drinking guidelines are usually set by blue ribbon committees based on curves showing relative risk of different levels of alcohol use for key alcohol-attributable disease categories such as liver cirrhosis, stroke or various types of cancer. This approach has certain limitations, as there is no easy way to combine the various risk curves and even for a combined risk curve there is no clear threshold, as all summary risk curves for alcohol tend to increase monotonically after small quantities of consumption. Therefore the present report chose to base risk estimations on the (absolute) lifetime risk of dying, following an approach applied by the developers of the Australian low risk guidelines for alcohol consumption. The lifetime risk approach has three advantages: firstly, absolute risks are easier to understand and clearer to communicate. Secondly, there are already standards in many societies and internationally about acceptable lifetime risk, both for voluntary risk and for involuntary risk. Thirdly, it allows comparisons of lifetime risk of alcohol with other risk factors. This report presents calculations for lifetime absolute risk for various levels of drinking for seven European countries. \n \nThis report was produced for the National Institute for Health and Welfare, Finland, and arises from the Joint Action on Reducing Alcohol Related Harm (RARHA) which has received funding from the European Union, in the framework of the Health Programme (2008-2013)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".