Call to action—Pediatric MASLD requires immediate attention to curb health crisis
Bibliographic record
Abstract
Pediatric metabolic dysfunction-associated steatotic liver disease (MASLD) has become more prevalent on a global scale over the last decades and is associated with significant comorbidities in childhood and a 40-fold higher risk of early mortality in young adulthood. MASLD has now become the leading indication for liver transplantation in young adults in the United States. However, pediatric MASLD is still perceived as an indolent condition by many patients, families, and clinicians. In this Call to Action, we aim to raise awareness of pediatric MASLD as a public health crisis. Herein, we describe insufficient screening and disease staging practices, and a lack of accurate non-invasive tests and effective pharmacotherapy, both stemming from a paucity of multicenter clinical trials in pediatric MASLD. We provide clear steps to address this public health emergency by promoting awareness campaigns, educating and empowering patients and families, addressing barriers including access to care, nutritional and exercise support programs, establishing multidisciplinary care, launching community initiatives, and conducting clinical trials in pediatric MASLD for an age-based evaluation of novel diagnostic and therapeutic options. We conclude by highlighting the urgent need for comprehensive public health policies to control the tide of pediatric MASLD and call upon stakeholders to act now.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".