Distinguishing nonalcoholic steatohepatitis from fatty liver: serum‐free fatty acids, insulin resistance, and serum lipoproteins
Bibliographic record
Abstract
OBJECTIVES: The prognosis of nonalcoholic fatty liver disease is determined by liver biopsy; steatohepatitis can be progressive whereas fatty liver is benign. Insulin resistance and increased hepatic-free fatty acids are central to the pathophysiology of this disorder. Our objective was to assess whether serum-free fatty acids, lipoproteins, and insulin resistance are increased in steatohepatitis compared with fatty liver and healthy controls, and thus may be potential noninvasive markers for liver disease severity. METHODS: Fifteen subjects with biopsy proven nonalcoholic steatohepatitis, 15 with histological fatty liver, and 15 healthy controls were enrolled. Fasting serum glucose and insulin levels, serum-free fatty acids, HDL, LDL, and cholesterol were collected from each subject. Insulin resistance was calculated using the homeostasis assessment model. RESULTS: Insulin resistance, LDL, and cholesterol-to-HDL ratio values were significantly higher in steatohepatitis, whereas HDL was significantly lower compared with both fatty liver and controls. Free fatty acids were similar in all groups. CONCLUSIONS: Along with insulin resistance, serum LDL, and cholesterol-to-HDL ratio values increase with worsening severity of liver histology, and serum HDL values decline. Free fatty acids, however, do not vary between groups.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".