FISPGHAN statement on the global public health impact of metabolic dysfunction‐associated steatotic liver disease
Bibliographic record
Abstract
As rates of obesity rise worldwide, incidence of metabolic dysfunction-associated steatotic liver disease (MASLD), formerly referred to as nonalcoholic fatty liver disease, is increasing, worsening the burden of healthcare systems. The council of the Federation of International Societies for Pediatric Gastroenterology, Hepatology, and Nutrition (FISPGHAN) identified the topic of MASLD epidemiology, treatment, and prevention as a global priority issue to be addressed by an expert team, with the goal to describe feasible and evidence-based actions that may contribute to reducing MASLD risk. The FISPGHAN member societies nominated experts in the field. The FISPGHAN council selected and appointed members of the expert team and a chair. The subtopics included in this manuscript were chosen through a consensus of the experts involved. We review the epidemiology, natural history, and screening and management. We further expand to relevant public health measures aimed at MASLD prevention, including identifying interventions that could reduce risk factors (environmental and iatrogenic), optimize maternal and newborn health, and support healthier lifestyles for older children and adolescents on a local, national, and international scale. While recognizing that various aspects of population health and public policy can shape MASLD risk, we also review what we can do on an individual level to support our patients to reduce the significant burden of this ever rising disease in pediatrics.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".