Non-alcoholic fatty liver disease–a chronic disease of the 21st century
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) comprises a spectrum of metabolic states ranging from simple steatosis to inflammation with associated fibrosis to cirrhosis. Though accumulation of hepatic fat is not associated with a significant increase in mortality rates, hepatic inflammation is, as this augments the risk of terminal liver disease, i.e., cirrhosis, hepatic decompensation (liver failure) and/or hepatocellular carcinoma. Disease progression is usually slow, over a decade or more and, for the most part, remains asymptomatic. Recent estimates suggest that the global prevalence of NAFLD is high, about one in four. In most cases, NAFLD overlaps with overweight, obesity, cardiovascular disease and the metabolic syndrome with numerous contributing parameters including a dysregulation of adipose tissue, insulin resistance, type 2 diabetes, changes in the gut microbiome, neuronal and hormonal dysregulation and metabolic stress. NAFLD is diagnosed incidentally, despite its high prevalence. Non-invasive imaging techniques have emerged, making it possible to determine degree of steatosis as well asfibrosis. Despite this, the benefit of routine diagnostics remains uncertain. A better understanding of the (molecular) pathogenesis of NAFLD is needed combined with long-term studies where benefits of treatment can be assessed to determine cost-benefit ratios. This review summarizes the current state of knowledge and possible areas of treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".