Updated recommendations for the management of metabolic dysfunction–associated steatotic liver disease (MASLD) by the Latin American working group
Bibliographic record
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the leading causes of chronic liver disease globally. Based on the 2023 definition, MASLD is characterized by the presence of metabolic dysfunction and limited alcohol consumption (<140 grams/week for women, <210 grams/week for men). Given the significant burden of MASLD in Latin America, this guidance was developed by the Latin American Association for the Study of the Liver (ALEH) Working Group to address key aspects of its clinical assessment and therapeutic strategies. In Latin America, ultrasonography is recommended as the initial screening tool for hepatic steatosis due to its accessibility, while Fibrosis-4 (FIB-4) is preferred for fibrosis risk stratification, with further evaluation using more specific techniques (i.e., vibration-controlled transient elastography or Enhanced Liver Fibrosis [ELF] test). A Mediterranean diet is advised for all MASLD patients, with a target of 7-10% weight loss for those with excess weight. Complete alcohol abstinence is recommended for patients with significant fibrosis, and smoking cessation is encouraged regardless of fibrosis stage. Pharmacological options should be tailored based on the presence of steatohepatitis, liver fibrosis, excess weight, and diabetes, including resmetirom, incretin-based therapies, pioglitazone, and sodium-glucose cotransporter-2 inhibitors. Bariatric surgery may be considered for MASLD patients with obesity unresponsive to lifestyle and medical interventions. Hepatocellular carcinoma screening is advised for all cirrhotic patients, with consideration given to those with advanced fibrosis based on individual risk. Finally, routine cardiovascular risk assessment and proper diabetes prevention and management remain crucial for all patients with MASLD.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".