Quantifying the global contribution of alcohol consumption to cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: The global impact of alcohol consumption on deaths due to cardiomyopathy (CM) has not been quantified to date, even though CM contains a subcategory for alcoholic CM with an effect of heavy drinking over time as the postulated underlying causal mechanism. In this feasibility study, a model to estimate the alcohol-attributable fraction (AAF) of CM deaths based on alcohol exposure measures is proposed. METHODS: A two-step model was developed based on aggregate-level data from 95 countries, including the most populous (data from 2013 or last available year). First, the crude mortality rate of alcoholic CM per 1,000,000 adults was predicted using a negative binomial regression based on prevalence of alcohol use disorders (AUD) and adult alcohol per capita consumption (APC) (n = 52 countries). Second, the proportion of alcoholic CM among all CM deaths (i.e., AAF) was predicted using a fractional response probit regression with alcoholic CM crude mortality rate (from Step 1), AUD prevalence, APC per drinker, and Global Burden of Disease region as predictions. Additional models repeated these steps by sex and for the wider Global Burden of Disease study definition of CM. RESULTS: There were strong correlations (>0.9) between the crude mortality rate of alcoholic CM and the AAFs, supporting the modeling strategy. In the first step, the population-weighted mean crude mortality rate was estimated at 8.4 alcoholic CM deaths per 1,000,000 (95% CI: 7.4-9.3). In the second step, the global AAFs were estimated at 6.9% (95% CI: 5.4-8.4%). Sex-specific figures suggested a lower AAF among females (2.9%, 95% CI: 2.3-3.4%) as compared to males (8.9%, 95% CI: 7.0-10.7%). Larger deviations between observed and predicted AAFs were found in Eastern Europe and Central Asia. CONCLUSIONS: The model proposed promises to fill the gap to include AAFs for CM into comparative risk assessments in the future. These predictions likely will be underestimates because of the stigma involved in all fully alcohol-attributable conditions and subsequent problems in coding of alcoholic CM deaths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".