Tests for diagnosing and monitoring non-alcoholic fatty liver disease in adults
Bibliographic record
Abstract
At a routine health check arranged by his company, a 52 year-old sedentary male computer programmer was found to have a serum alanine aminotransferase (ALT) concentration of 68 IU/L (normal 0-40 IU/L), and a triglyceride concentration of 1.9 mmol/L.His fasting plasma glucose levels were 5.8 mmol/L and other basic liver, renal and lipid blood tests were normal.He had an unremarkable medical history and took no regular medications, did not smoke and consumed <7 units of alcohol/week.Clinical examination was unremarkable.His body mass index was 29 kg/m 2 ; waist circumference 102 cm and blood pressure 134/88 mmHg. What is the next investigation?His general practitioner requested a liver ultrasonography (confirming the presence of hepatic steatosis) and a repeat serum ALT measurement was 62 IU/L.Other blood tests (including serology for hepatitis B and C viruses, liver auto-antibodies and ferritin) excluded other causes of liver dysfunction.The patient is likely to have non-alcoholic fatty liver disease (NAFLD).Box 1 describes how patients with NAFLD usually present.When LFTs (e.g.serum aminotransferases such as serum ALT levels) are increased (above the laboratory recommendation for the upper limit of normal), patients should be further investigated to diagnose (or exclude) NAFLD.Figure 1 illustrates a potential investigative pathway for diagnosing NAFLD and for identifying other common causes of chronic liver disease.When patients have any of the common cardiometabolic risk factors shown in Box 2 plus abnormal LFTs, it is likely the diagnosis is NAFLD (in the absence of other risk factors for liver disease shown in Box 3).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".