Impact of baseline FIB-4 score on efpeglenatide benefits on cardiovascular outcomes in people with type 2 diabetes: a participant-level exploratory analysis of the AMPLITUDE-O trial
Bibliographic record
Abstract
AIMS: To estimate the incidence of major adverse cardiovascular events (MACE), expanded MACE, and MACE or Death across Fibrosis- 4 score (FIB-4) categories in people with type 2 diabetes and to determine whether efpeglenatide's effect varies with increasing FIB-4 severity. MATERIALS AND METHODS: AMPLITUDE-O trial data were used to estimate the relationship of FIB-4 score categories to the hazard of MACE, expanded MACE, and MACE or death. Interactions on these outcomes between baseline FIB-4 score, and between FIB-4 score and efpeglenatide were also assessed. RESULTS: Baseline FIB-4 score was available for 4059 participants (99.6%) allowing subdivision of the population in tertiles. During a median follow-up of 1.8 years, numerical increases in the incidence of all 3 outcomes did not change significantly across tertiles of FIB-4 score (P for trend ≥ 0.25) with negligible relationship of the score to incident outcomes (MACE HR, per 1 SD higher score, 95% CI: 1.00, 0.89-1.13). Efpeglenatide's effect on all MACE outcomes did not vary across FIB-4 tertiles (all interaction p values ≥ 0.64). CONCLUSIONS: In high-risk people with type 2 diabetes, the degree of liver fibrosis, as estimated by FIB-4 score, was not related to incident cardiovascular outcomes. The beneficial effect of efpeglenatide on these outcomes is independent of FIB-4 category.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".