AASLD Practice Statement on the evaluation and management of metabolic dysfunction–associated steatotic liver disease in children
Bibliographic record
Abstract
Given the high prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) in children and its distinct epidemiological, clinical, and histopathological differences from adult disease, the American Association for the Study of Liver Diseases (AASLD) commissioned this pediatric-focused evidence-based practice statement. A multidisciplinary writing group—encompassing expertise in pediatric hepatology, gastroenterology, endocrinology, and liver pathology—conducted a comprehensive PubMed literature search for studies published through March 6, 2024, involving pediatric participants (ages 0–18 y) with NAFLD or MASLD. The review addressed epidemiology, pathophysiology, natural history, screening, diagnosis, treatment, comorbidity management, outcome monitoring, and transition of care. Using the highest available level of evidence—randomized controlled trials, large observational cohort studies, systematic reviews, and meta-analyses—30 evidence-guided practice statements were developed. Where high-quality evidence was lacking, expert consensus was employed, and critical knowledge gaps were identified to inform future research priorities. This document highlights key concepts relevant to pediatric MASLD, especially regarding diagnostic criteria, noninvasive assessment tools, and therapeutic approaches. It also discusses the implications of the 2023 nomenclature revision, which emphasizes evaluating both hepatic steatosis and cardiometabolic risk factors. With increasing recognition of MASLD’s cardiometabolic burden and long-term health consequences, this practice statement provides a structured framework to advance clinical care and research in pediatric MASLD.
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".