Prevention and control of risk factors in metabolic and alcohol-associated steatotic liver disease
Bibliographic record
Abstract
Steatotic liver disease (SLD), including metabolic dysfunction-associated steatotic liver disease (MASLD) and alcohol-associated liver disease (ALD), is the primary cause of illness and mortality. In particular, MASLD affects more than 30% of the global population, while ALD accounts for 5.1% of all diseases and injuries worldwide. The SLD spectrum includes a variety of clinical conditions, from mild fatty liver and inflammation to different stages of liver fibrosis. Additionally, both conditions (MASLD and ALD) can be complicated by hepatocellular carcinoma (HCC), while around one-third of ALD patients can also develop at least one alcohol‐associated hepatitis (AH) episode. Both of these diseases are also associated with multiple extrahepatic complications, such as cardiovascular disease, chronic kidney disease, and malignancies. In MASLD, the rapid rise in global obesity and type 2 diabetes mellitus (T2DM) prevalence due to Westernized lifestyles has led to an increase in the prevalence of MASLD. Thus, the prevention and control of cardiometabolic risk factors (CMRFs) are the cornerstone of its treatment. Hypertension and atherogenic dyslipidemia are also important CMRFs associated with MASLD. Susceptible individuals with MASLD are adversely affected by even a small amount of alcohol consumption (though there is no agreed definition of a small amount), increasing the risk of severe outcomes and a faster progression of liver disease. This review explores factors that play a role in the development of SLD, especially focusing on the management of CMRFs and levels of alcohol use to prevent liver disease progression.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".