Evaluation of fibrosis in patients with nonalcoholic fatty liver disease
Bibliographic record
Abstract
Evaluation of fibrosis in patients with nonalcoholic fatty liver disease ଝ Evaluación de la fibrosis en pacientes con enfermedad de hígado graso no alcohólicoNonalcoholic fatty liver disease (NAFLD), or metabolic dysfunction-associated fatty liver disease (MAFLD), according to a new consensus-driven nomenclature proposed by a group of experts 1 , has an estimated prevalence of 25 to 30% in the adult population and is the most frequent cause of abnormal liver function tests (LFTs) seen in the primary healthcare setting in Europe and North America 2 .The pathophysiology of NAFLD is complex but appears to be a continuum of entities, ranging from obesity to metabolic syndrome and diabetes.Environmental factors can affect the genetic expression, causing weight gain.When the expansion capacity of the subcutaneous tissue is reached, it produces an increase in free fatty acid (FFA) mobilization, resulting in visceral and ectopic fat deposition.The increase in FFA deposition promotes insulin resistance (IR), inhibiting insulin-mediated glucose uptake.Thus, adipose tissue IR facilitates lipolysis and increases the flow of FFAs to the liver, inducing hepatic IR and enhancing glucose production, hepatic de novo lipogenesis, and atherogenic dyslipidemia 3 .Those pathogenic mechanisms are reflected in the prevalence of NAFLD, in parallel with the prevalence of obesity, metabolic syndrome, and diabetes.The prevalence of obesity in patients with NAFLD is 51%, the prevalence of obesity in patients with nonalcoholic steatohepatitis (NASH) is 81%, and the prevalence of NAFLD in patients with obesity varies from 60 to 95% 4---6 .NAFLD has a broad clinical spectrum, ranging from simple steatosis with fatty infiltration of ≥ 5% of the liver parenchyma and no ballooning degeneration or inflammation, to NASH, with necroinflammation and hepatocellular damage (ballooning degeneration of hepatocytes, Mallory ଝ Please cite this article as:
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".