Global Consensus Recommendations for Metabolic Dysfunction-Associated Steatotic Liver Disease and Steatohepatitis
Bibliographic record
Abstract
BACKGROUND & AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) and steatohepatitis (MASH) are associated with adverse clinical outcomes, impaired health-related quality of life, and significant economic burden. The growing burden of MASLD and MASH has led to the publication of a large number of MASLD/MASH guidelines by national and international societies. However, important differences among the recommendations have created confusion, contributing to a low implementation rate and suboptimal management of MASLD and MASH. Creating a consensus recommendation has become more important since the approval of a selective agonist of thyroid hormone β receptor (resmetirom) for MASH treatment in the United States. We built a consensus among the most recently published recommendations for MASLD/MASH. METHODS: A comprehensive search for MASLD and MASH guidelines, guidance documents, or similar publications from January 2018 to January 2025 using PubMed, Embase, Web of Science, and society websites was conducted. Each selected document was assessed across 8 specific domains with 145 variables. Variables with <50% concordance were used for the Delphi statement development. A supermajority threshold of 67% was set for statement acceptance. RESULTS: There were 61 documents published from 2018 through January 2025. Four rounds of Delphi were conducted: 46 statements were generated for Round 1, 32 statements for Round 2, 16 statements for Round 3, and 8 statements for Round 4, whereby 100% of statements achieved a greater than 90% agreement. All final consensus recommendations were summarized in tables and algorithms. CONCLUSIONS: Our study provides an extensive set of recommendations generated based on a comprehensive review of the most recent MASLD/MASH guidelines and a consensus-building process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".