From Shadows to Spotlight: Exploring the Escalating Burden of Alcohol-Associated Liver Disease and Alcohol Use Disorder in Young Women
Bibliographic record
Abstract
INTRODUCTION: The burden of alcohol-related complications is considerable, particularly alcohol-associated liver disease and alcohol use disorder (AUD). However, there are deficiencies in comprehensive epidemiological research focusing on these issues, especially among young women who display higher susceptibility to such complications compared with their male counterparts. We thus aimed to determine the global burden of these conditions in this vulnerable group. METHODS: Leveraging data from the Global Burden of Disease Study 2019, we analyzed the prevalence, mortality, and disability-adjusted life years of alcohol-associated cirrhosis (AC), liver cancer from alcohol, and AUD in young women. The findings were categorized by region, nation, and sociodemographic index. RESULTS: The highest age-standardized prevalence rates were observed in AUD (895.96 [95% uncertainty interval (UI) 722.6-1,103.58]), followed by AC (65.33 [95% UI 48.37-86.49]) and liver cancer from alcohol (0.13 [95% UI 0.09-0.19]) per 100,000 people. The highest age-standardized mortality rates were observed in AC (0.75 [95% UI 0.55-0.97]), followed by AUD (0.48 [95% UI 0.43-0.53]) and liver cancer from alcohol (0.06 [95% UI 0.04-0.09]). The highest burdens of AC and AUD were observed in Central Europe, whereas the high-income Asia Pacific had the highest burden of liver cancer from alcohol. DISCUSSION: Throughout the past decade, the trend of AUD varied among regions while the impact of alcohol-associated liver disease has increased, requiring urgent public health strategy to mitigate these complications, particularly in female patients in Europe and the Asia-Pacific region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".