A Significant Effect of Pemafibrate on Hepatic Steatosis and Fibrosis Indexes in Patients With Hypertriglyceridemia
Bibliographic record
Abstract
Background: We previously reported that the selective peroxisome proliferator-activated receptor alpha modulator, pemafibrate, significantly reduced serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT) and gamma-glutamyl transferase (GGT) and significantly increased serum albumin levels at 3, 6 and 12 months after the start of pemafibrate, with an improvement of atherogenic dyslipidemia, in patients with hypertriglyceridemia. Methods: analysis of our previous data obtained from patients with hypertriglyceridemia who had been prescribed pemafibrate continuously for 1 year or longer. We compared the indexes for hepatic steatosis (hepatic steatosis index (HSI)) and fibrosis (nonalcoholic fatty liver disease (NAFLD) fibrosis score (NFS), AST to platelet ratio index (APRI) and FIB-4 index) at baseline with the data at 1 year after the start of pemafibrate. Results: Pemafibrate significantly reduced HSI at 1 year after the start of pemafibrate. NFS did not show a significant change after 1 year. However, APRI was significantly reduced by pemafibrate after 1 year. FIB-4 index significantly decreased in patients with baseline FIB-4 index ≥ 1.45 at 1 year after the start of pemafibrate. HSI at baseline tended to be negatively correlated with change in HSI after 1 year. There was no significant correlation between NFS at baseline and change in this score after 1 year. APRI and FIB-4 index at baseline were significantly and negatively correlated with changes in APRI and FIB-4 index at 1 year after the start of pemafibrate. Conclusions: The 1-year pemafibrate treatment improved hepatic steatosis and fibrosis indexes in patients with hypertriglyceridemia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".