The Impact of Alcohol Consumption on Cardiovascular Health: Myths and Measures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past several decades, the prevalence of cardiovascular disease (CVD) has nearly doubled, and alcohol has played a major role in the incidence of much of it. Alcohol has also been attributed in deaths due to infectious diseases, intentional and unintentional injuries, digestive diseases, and several other non-communicable diseases, including cancer. The economic costs of alcohol-associated health outcomes are significant at the individual as well as the country level. Risks due to alcohol consumption increase for most cardiovascular diseases, including hypertensive heart disease, cardiomyopathy, atrial fibrillation and flutter, and stroke. The widespread message for over 30 years has been to promote the myth that alcohol prolongs life, chiefly by reducing the risk of coronary heart disease (CHD). Lack of universal advice and stringent policy measures have contributed towards increased uptake and easy availability of alcohol. The WHO has called for a 10% relative reduction in the harmful use of alcohol between 2013-2025. However, lack of investment in proven alcohol control strategies, as well as persistence of misinformation and industry interference, have hindered the efforts of public health professionals to make sufficient progress in reducing alcohol related harms and death.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it