Mortality from diseases, conditions and injuries where alcohol is a necessary cause in the <scp>A</scp>mericas, 2007–09
Bibliographic record
Abstract
AIMS: To describe mortality from diseases, conditions and injuries where alcohol was a necessary cause in selected countries in the Americas. DESIGN: A descriptive, population-based study. SETTING: The data come from 16 countries in North, Central and South America for the triennium 2007-09 (latest available data). PARTICIPANTS/CASES: A total of 238 367 deaths were analyzed. MEASUREMENTS: We calculated age-adjusted and age-specific mortality rates by sex and country using the Pan American Health Organization (PAHO) mortality database. FINDINGS: The annual average of deaths where alcohol was a necessary cause in the 16 countries was 79, 456 (men comprised 86% and women 14%). People aged 40-59 years represented 55% overall. Most deaths were due to liver diseases (63% overall) and neuropsychiatric disorders (32% overall). Overall age-adjusted rates/100,000 were higher in El Salvador (27.4), Guatemala (22.3), Nicaragua (21.3) and Mexico (17.8) and lower in Colombia (1.8), Argentina (4.0) and Canada (5.7). The age groups at the highest risk were 54-59 to 64-69 years in most countries. In Guatemala, El Salvador and Nicaragua the rates increased earlier, among those aged 30-49 years. Male rates were higher than female rates in all countries, but the male : female ratio varied widely. CONCLUSIONS: Diseases, conditions or injuries where alcohol is a necessary cause are an important cause of premature mortality in the Americas, especially among men. Some countries show high risk of dying from this group of causes.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".