Design and Baseline Characteristics of the Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease Trial
Bibliographic record
Abstract
BACKGROUND: Among diabetics, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality, and progression of their underlying disease. Finerenone is a novel, non-steroidal, selective mineralocorticoid-receptor antagonist which has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD), while revealing only a low risk of hyperkalemia. However, the effect of finerenone on renal and CV outcomes has not been investigated in long-term trials yet. METHODS: The Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease -(FIDELIO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important renal and CV outcomes in T2D patients with CKD. FIDELIO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 5.5 years. FIDELIO-DKD randomized 5,734 patients with an estimated glomerular filtration rate (eGFR) ≥25-<75 mL/min/1.73 m2 and albuminuria (urinary albumin-to-creatinine ratio ≥30-≤5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of primary outcome (overall two-sided significance level α = 0.05), the composite of time to first occurrence of kidney failure, a sustained decrease of eGFR ≥40% from baseline over at least 4 weeks, or renal death. CONCLUSION: FIDELIO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of renal and CV events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".