Original Contribution Determination of Lifetime Injury Mortality Risk in Canada in 2002 by Drinking Amount per Occasion and Number of Occasions
Bibliographic record
Abstract
Injury is the leading cause of alcohol-attributable mortality in Canada. Risk is determined by amount consumed per occasion and accumulates across drinking episodes. The authors estimated alcohol-attributable injury mor-tality in Canada for 2002 by combining the absolute risk of injury unrelated to alcohol with relative risks that were specific to gender and consumption per occasion, while taking into account lifetime number of drinking occasions. The absolute risk increased as number of drinking occasions and number of drinks per occasion increased. The absolute risk remained relatively low at fewer than 2 drinking occasions per month, regardless of number of drinks. Absolute risk levels reached 1 in 1,000 at 5 or more drinks once per month for men and at 5–7 drinks once per month for women. The probability of mortality was 1 in 100 for all levels of consumption above 3 drinks 3 times per week for men and above 5 drinks 3 times per week for women. No safe level of consumption is recommended based on these results, although risk is much lower for consuming 3 standard drinks or less fewer than 3 times per week. Absolute risk reflects long-term effects of drinking patterns and is important for risk-communication and alcohol-control policy. alcohol drinking; mortality; risk assessment; wounds and injuries Abbreviation: BAC, blood alcohol concentration. Editor’s note: An invited commentary on this article ap-
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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 | 0.014 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".