Managing <scp>MASLD</scp> Through Preventive Hepatology: Integrating Policy Reform, Public Health and Personalised Care
Bibliographic record
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is growing in prevalence around the world, with a current global prevalence rate of 38%. Although MASLD serves as an umberlla term, its subtype of metabolic dysfunction associated steatohepatitis (MASH) with a prevalence of 5-7%, can lead to adverse liver outcomes including cirrhosis and liver mortality. However, prevalence rates for MASLD/MASH vary by country and region of the world. With the increasing rates of type 2 diabetes and obesity, MASLD/MASH is increasing and is currently among the top causes of hepatocellular carcinoma and an indication for liver transplantation in the United States. Therefore, the care model is shifting to prevention given this large clinical, economic and humanistic burden of this liver disease. As in other noncommunicable diseases, interventional priorities for policymakers should be focused on building infrastructure that supports physical activity and healthy food choices as well as access to approved treatments for MASLD. At the same time, identifying individuals at risk for adverse outcomes using non-invasive tests and developing individual care plans that address the needs of each patient with MASLD, including their mental and physical health, should be a focus for healthcare providers. Furthermore, raising awareness among patients, the public and healthcare providers continues to be a crucial need. This report will provide recommendations for policymakers to provide the needed interventions to reverse the current trajectory of this liver disease.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".