Cardiorenal outcomes by indices of liver steatosis and fibrosis in individuals with type 2 diabetes and atherosclerotic cardiovascular disease: Analyses from <scp>VERTIS CV</scp> , a randomized trial of the <scp>sodium‐glucose cotransporter‐2</scp> inhibitor ertugliflozin
Bibliographic record
Abstract
AIM: To conduct a post hoc analysis to explore indices of hepatic steatosis/fibrosis and cardiorenal outcomes in the VERTIS CV study. MATERIALS AND METHODS: Patients with type 2 diabetes and atherosclerotic cardiovascular (CV) disease were randomized to ertugliflozin or placebo. Liver steatosis and fibrosis were assessed post hoc using the hepatic steatosis index (HSI) and fibrosis-4 (FIB-4) index to explore associations with cardiorenal outcomes (ertugliflozin and placebo data pooled, intention-to-treat analysis set). Cardiorenal outcomes (major adverse CV events [MACE]; hospitalization for heart failure [HHF]/CV death; CV death; HHF; and a composite kidney outcome) were stratified by baseline HSI and FIB-4 quartiles (Q1-Q4). Change in liver indices and enzymes over time were assessed (for ertugliflozin vs. placebo). RESULTS: , HSI 44.0 and FIB-4 score 1.34. The hazard ratios (HRs) for MACE, HHF/CV death, CV death, and HHF by FIB-4 score quartile (Q4 vs. Q1) were 1.48 (95% confidence interval [CI] 1.25, 1.76), 2.0 (95% CI 1.63, 2.51), 1.85 (95% CI 1.45, 2.36), and 2.94 (95% CI 1.98, 4.37), respectively (P < 0.0001 for all). With HSI, the incidence of HHF was higher in Q4 versus Q1 (HR 1.52 [95% CI 1.07, 2.17]; P < 0.05). The kidney composite outcome did not differ across FIB-4 or HSI quartiles. Liver enzymes and HSI decreased over time with ertugliflozin. CONCLUSION: In VERTIS CV, higher FIB-4 score was associated with CV events. HSI correlated with HHF. Neither measure was associated with the composite kidney outcome. Ertugliflozin was associated with a reduction in liver enzymes and HSI.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".