Non-invasive diagnostic methods for non-alcoholic fatty liver disease
Bibliographic record
Abstract
Background: NAFLD is one of the most common causes of liver disease worldwide. It is a spectrum of disease characterized by macrovesicular steatosis of the liver that ranges from simple fatty liver (steatosis), to non-alcoholic steatohepatitis (NASH). NASH may eventually evolve to cirrhosis and end stage complication. Liver biopsy has long been considered the gold standard of reference to diagnose NAFLD but it is costly and invasive. Recently, non-invasive methods have been proposed. Aims and methods: The aim of this study was to investigate the accuracy of non-invasive methods including (Ultrasound, computed tomography scan, Xenon-133 scan, Hepatic steatosis index, Fibroscan, NAFLD fibrosis score, APRI index, and FIB-4 index) and their combination to diagnose steatosis and to diagnose significant liver fibrosis (>F2) and cirrhosis (F4) as compared to liver biopsy. We conducted a retrospective study of 114 NASH patients (79 males, mean age 49.6±10.6). All had adequate liver histology. Results: The distribution of fibrosis stage was as follows: F0-F1= 50%, F2=16.8%, F3=19.2%, F4=14%. The distribution of steatosis grade was as follows: grade 0-1=16%, grade2=53.3%, grade3=30.7%. The following tests correlated with fibrosis: APRI index (r=0.554), FIB-4(r=0.555), NAFLD fibrosis score (r=0.473), Fibroscan(r=0.586) and Hepatic Steatosis Index (HSI) (r=0.245). The FIB-4 and APRI index showed the best diagnostic accuracy for significant fibrosis as indicated by an Area Under the Curve (AUC) of 0.801 and 0.782, respectively. The FIB-4 showed the best AUC= 0.886 for cirrhosis. None of the following tests US, CT, HSI, and xenon-133 scan were considered correlated significantly. The best combination algorithm for the detection of cirrhosis was gender and FIB-4 with an AUC of 0.8937. Conclusion: this study demonstrates that non-invasive methods for liver fibrosis are accurate to diagnose >F2 and F4. Severe steatosis cannot be reliably diagnosed by non-invasive methods. Notably, a combination of FIB-4 and gender significantly improves the performance of the single method for cirrhosis. These methods may help reducing the number of liver biopsies stratifying NASH patients that should start a screening program for HCC and esophageal varices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".