Deaths Associated with High-Volume Drinking of Alcohol among Adults in Canada in 2002: A Need for Primary Care Intervention?
Bibliographic record
Abstract
This study estimates risks of mortality associated with high-volume drinking for Canada in 2002 by age and sex. Distribution of exposure was taken from a major Canadian survey and corrected for per capita consumption from production and sales. High-volume drinking was defined as a daily consumption of ⩾40 grams of pure alcohol (at least 3 Canadian drinks) for men and ⩾20 grams of pure alcohol (at least 1.5 Canadian drinks) for women. Risk relations were taken from the published literature and combined with exposure to calculate age-and sex-specific alcohol-attributable fractions for high-volume drinking. Information on mortality was obtained from Statistics Canada and combined with alcohol-attributable fractions to estimate the overall mortality due to alcohol. About 4,950 net deaths (3,236 in those below 70 years of age) were due to high-volume drinking of alcohol in Canada in 2002. This constituted 2.2% (5.0% among those below 70 years of age) of all deaths. The net deaths were composed of 5,717 deaths caused and 767 deaths prevented. There was an age gradient, with the net deaths highest in 45–59 years age group. About 70.6% (5,717/8,103) of the overall deaths caused by alcohol were the result of high-volume drinking. Overall, the net impact of high-volume drinking of alcohol consumption on mortality in Canada is high. Policies should strive to reduce the burden of high-volume alcohol consumption. In addition to alcohol control measures, individual level interventions should be implemented in primary care to significantly reduce such burden.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".