Alcohol-related liver disease: A global perspective
Bibliographic record
Abstract
Alcohol-associated liver disease (ALD) represents one of the deadliest yet preventable consequences of excessive alcohol use. It represents 5.1 % of the global burden of disease, mainly involving the productive-age population (15-44 years) and leading to an increased mortality risk from traffic road injuries, suicide, violence, cardiovascular disease, neoplasms, and liver disease, among others, accounting for 5.3 % of global deaths. Daily alcohol consumption, binge drinking (BD), and heavy episodic drinking (HED) are the patterns associated with a higher risk of developing ALD. The escalating global burden of ALD, even exceeding what was predicted, is the result of a complex interaction between the lack of public policies that regulate alcohol consumption, low awareness of the scope of the disease, late referral to specialists, underuse of available medications, insufficient funds allocated to ALD research, and non-predictable events such as the COVID-19 pandemic, where increases of up to 477 % in online alcohol sales were registered in the United States. Early diagnosis, referral, and treatment are pivotal to achieving the therapeutic goal in patients with alcohol use disorder (AUD) and ALD, where complete alcohol abstinence and prevention of alcohol relapse are expected to enhance overall survival. This can be achieved through a combination of cognitive behavioral, motivational enhancement and pharmacological therapy. Furthermore, the appropriate use of available pharmacological therapy and implementation of public policies that comprehensively address this disease will make a real difference.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".