{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T18:56:22Z","timestamp":1792090582107,"version":"4.4.0"},"reference-count":24,"publisher":"Baishideng Publishing Group Inc.","issue":"36","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["World J Gastroenterol"],"abstract":"<jats:p>Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the leading cause of chronic liver disease in children, affecting up to 38% with obesity of children. With the global shift from non-alcoholic fatty liver disease (NAFLD) to MASLD using affirmative criteria (hepatic steatosis plus \u2265 1 cardiometabolic risk factor) and approximately 99% concordance in pediatrics, the development of non-invasive fibrosis tools is accelerating. Yao et al  report a machine-learning \u201cchronic MASLD with fibrosis (CH-MASLD-Fib)\u201d score for advanced fibrosis with area under the receiver operating characteristic curve (AUROC) of 0.92. While timely, we urge caution. First, high accuracy from a single-center cohort signals overfitting: Complex models can learn cohort-specific noise and fail to generalize. Consistent with this, established pediatric scores (NAFLD fibrosis score, fibrosis-4, pediatric NAFLD fibrosis score) perform modestly (AUROC: Approximately 0.6-0.7), and aspartate aminotransferase-to-platelet ratio index is variable, raising concern that CH-MASLD-Fib\u2019s result reflects a statistical artifact. Second, MASLD epidemiology varies by ethnicity (highest in Hispanic, lower in Black children); a model derived in a mono-ethnic Chinese cohort may misclassify other populations. Third, clinical utility and cost-effectiveness are unproven; dependence on specialized assays (e.g. , bile acids, cholinesterase) would limit access and increase cost. We recommend external validation in multi-ethnic cohorts, head-to-head comparisons with simple serum indices and elastography, and formal economic analyses. Until then, clinical judgment anchored in readily available markers and judicious, targeted liver biopsy remains paramount.<\/jats:p>","DOI":"10.3748\/wjg.v31.i36.112217","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T09:55:08Z","timestamp":1758189308000},"source":"Crossref","is-referenced-by-count":4,"title":["Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature"],"prefix":"10.3748","volume":"31","author":[{"given":"Toshifumi","family":"Yodoshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2060","published-online":{"date-parts":[[2025,9,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v31.i28.108321"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0140908"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v30.i47.5070"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2023.06.003"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1097\/HC9.0000000000000546"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v28.i5.605"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0104558"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1002\/hep.21496"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2009.05.033"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1097\/00042737-200604000-00012"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2022.885576"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1097\/MPG.0000000000003371"},{"key":"B13","unstructured":"Graphite Note.   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