Relationship between Liver Function Tests Levels with Degree of FibroScan Test in non-alcoholic Fatty Liver Disease
Bibliographic record
Abstract
Background and Objectives: Non-Alcoholic Fatty Liver Disease (NAFLD) occurs when liver fat content exceeds 5-10%.The initial stage is simple fatty liver, which can progress to alcoholic steatohepatitis and ultimately lead to cirrhosis of the liver.The first step in treatment is a weight loss diet.Methods: In this study, patients with non-alcoholic fatty liver disease who had undergone all necessary tests to rule out other causes of liver involvement, such as viral and autoimmune hepatitis and Wilson's disease, were evaluated.These patients were approved by a gastroenterologist and underwent a FibroScan over a six-month period to assess their condition.The initial checklist included demographic information (height and weight), blood pressure, history of alcohol consumption, and liver enzyme levels.Results: Among 86 participants, 25 (29.1%) had Grade 0 fatty liver, 39 (54.7%) had Grade 1, 14 (11.6%) had Grade 2, and 8 (4.7%) had Grade 3 fatty liver.Additionally, 8 patients had anemia, 3 (2.5%) had elevated bilirubin levels, 3 (2.5%) had iron deficiency, and only 1 patient had liver issues related to an autoimmune problem or specific disease.There was no significant relationship between the FibroScan score and enzyme levels in any gender. Conclusion:The prevalence of non-alcoholic fatty liver disease is higher in women than in men, and liver enzymes do not accurately reflect the degree of liver fibrosis.It is recommended that imaging methods, especially FibroScan, be used instead of routine enzyme level measurements to assess liver tissue conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".