Design of the COmbinatioN effect of FInerenone anD EmpaglifloziN in participants with chronic kidney disease and type 2 diabetes using a UACR Endpoint study (CONFIDENCE)
Bibliographic record
Abstract
BACKGROUND: Despite available interventions, people with type 2 diabetes (T2D) remain at risk of chronic kidney disease (CKD). Finerenone, a potent and selective nonsteroidal mineralocorticoid receptor antagonist, and sodium-glucose cotransporter 2 inhibitors (SGLT2is) can reduce both kidney and cardiovascular risks in people with CKD and T2D. Here we outline the design of a study to investigate whether dual therapy with finerenone and an SGLT2i is superior to either agent alone. METHODS: CONFIDENCE (NCT05254002) is a randomized, controlled, double-blind, double-dummy, international, multicenter, three-armed, parallel-group, 7.5 - to 8.5-month, Phase 2 study in 807 adults with T2D, stage 2-3 CKD and a urine albumin:creatinine ratio (UACR) ≥300-<5000 mg/g. The primary objective is to demonstrate that 6 months of dual therapy comprising finerenone and the SGLT2i empagliflozin is superior for reducing albuminuria versus either agent alone. Interventions will be once-daily finerenone 10 mg or 20 mg (target dose) plus empagliflozin 10 mg, or empagliflozin 10 mg alone, or finerenone 10 mg or 20 mg (target dose) alone. RESULTS: The primary outcome is a relative change from baseline in UACR among the three groups. Secondary outcomes will further characterize efficacy and safety, including changes in estimated glomerular filtration rate and incident hyperkalemia. CONCLUSIONS: CONFIDENCE is evaluating the safety, tolerability and efficacy of dual use of finerenone and an SGLT2i in adults with CKD and T2D. Should an additive effect be shown, early and efficient intervention with dual finerenone and SGLT2i therapy could slow disease progression and provide long-term benefits for people with CKD and T2D.
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".