Changing landscape of alcohol‐associated liver disease in younger individuals, women, and ethnic minorities
Bibliographic record
Abstract
Alcohol use is the most important determinant of the development of alcohol-associated liver disease (ALD) and of predicting long-term outcomes in those with established liver disease. Worldwide, the amount, type, and pattern of use of alcohol vary. Alcohol use and consequent liver disease have been increasing in certain ethnic groups especially Hispanics and Native Americans, likely due to variations in genetics, cultural background, socio-economic status, and access to health care. Furthermore, the magnitude and burden of ALD have been increasing especially in the last few years among females and young adults who are at the prime of their productivity. It is critical to recognize the problem and care for these patients integrating cultural aspects in liver clinics. At the federal level, a societal approach is needed with the implementation of public health policies aiming to reduce alcohol consumption in the community. By addressing these challenges and promoting awareness, we can strive to reduce the burden of ALD, especially in high-risk demographic groups to improve their long-term health outcomes. Finally, we need studies and quality research examining these changing landscapes of demographics in ALD as a basis for developing therapeutic targets and interventions to reduce harmful drinking behaviours in these high-risk demographic groups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".