The association of pattern of lifetime alcohol use and cause of death in the European Prospective Investigation into Cancer and Nutrition (EPIC) study
Bibliographic record
Abstract
BACKGROUND: There is limited evidence for an association between the pattern of lifetime alcohol use and cause-specific risk of death. METHODS: Multivariable hazard ratios were estimated for different causes of death according to patterns of lifetime alcohol consumption using a competing risks approach: 111 953 men and 268 442 women from eight countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study were included. Self-reported alcohol consumption at ages 20, 30, 40 or 50 years and at enrollment were used for the analysis; 26 411 deaths were observed during an average of 12.6 years of follow-up. RESULTS: The association between lifetime alcohol use and death from cardiovascular diseases was different from the association seen for alcohol-related cancers, digestive, respiratory, external and other causes. Heavy users (>5 drinks/day for men and >2.5 drinks/day for women), regardless of time of cessation, had a 2- to 5-times higher risk of dying due to alcohol-related cancers, compared with subjects with lifetime light use (≤1 and ≤0.5 drink/week for men and women, respectively). Compared with lifetime light users, men who used <5 drinks/day throughout their lifetime had a 24% lower cardiovascular disease mortality (95% confidence interval 2-41). The risk of death from coronary heart disease was also found to be 34-46% lower among women who were moderate to occasionally heavy alcohol users compared with light users. However, this relationship was only evident among men and women who had no chronic disease at enrollment. CONCLUSIONS: Limiting alcohol use throughout life is associated with a lower risk of death, largely due to cardiovascular disease but also other causes. However, the potential health benefits of alcohol use are difficult to establish due to the possibility of selection bias and competing risks related to diseases occurring later in life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".