Diagnosis of Nonalcoholic Fatty Liver Disease: Invasive versus Noninvasive
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the United States and many other parts of the world. Its prevalence continues to rise-currently affecting approximately 20 to 30% of adults and 10% of children in the United States. NAFLD represents a wide spectrum of conditions ranging from fatty liver, which in general follows a benign nonprogressive clinical course, to steatohepatitis or NASH, a more serious form of NAFLD that may progress to cirrhosis and end-stage liver disease. Although currently a combination of noninvasive clinically available laboratory and imaging tests may help in the diagnostic evaluation of a patient with suspected NAFLD, a liver biopsy remains the only reliable way to precisely diagnose NASH and establish the severity of liver injury and presence of fibrosis. It also provides important information regarding prognosis as well as response to therapeutic interventions. However, a liver biopsy is an invasive and costly procedure, which is poorly suited as a diagnostic test for a condition that may affect about one-third of the U.S. population. This review provides a concise overview of the role of liver biopsy versus noninvasive diagnostic tools for the differentiation of fatty liver from nonalcoholic steatohepatitis as well as for the determination of presence and extent of fibrosis. In particular, this review focuses on the methods currently available in daily clinical practice in hepatology and touches briefly on potential future markers under investigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".