<scp>MetALD</scp>: New Perspectives on an Old Overlooked Disease
Bibliographic record
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) and alcohol-associated liver disease (ALD) are the major contributors to the liver disease burden globally. The rise in these conditions is linked to obesity, type 2 diabetes, metabolic syndrome and increased alcohol consumption. MASLD and ALD share risk factors, pathophysiology and histological features but differ in their thresholds for alcohol use, and the ALD definition does not require the presence of metabolic dysfunction. A recent multi-society consensus overhauled the nomenclature of liver steatosis and introduced the term MetALD to describe patients with metabolic dysfunction who drink more than those with MASLD and less than those with ALD. This new terminology aims to enhance the understanding and management of liver disease but poses challenges, such as the need to accurately measure alcohol consumption in research and clinical practice settings. Recent studies show that MetALD has significant implications for patient management, as it is associated with increased mortality risks and more severe liver outcomes compared to MASLD alone. MetALD patients face increased risks of liver disease progression, cancer and cardiovascular disease. The diagnosis of MetALD involves the adequate quantification of alcohol use through standardised questionnaires and/or biomarkers as well as proper assessment of liver disease stage and progression risk using non-invasive tools including serologic markers, imaging, elastography techniques and genetic testing. Effective management requires addressing both metabolic and alcohol-related factors to improve outcomes. This review intends to provide a comprehensive overview of MetALD, covering pathogenesis, potential diagnostic approaches, management strategies and emerging therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".