Rationale and design of a randomised phase III registration trial investigating finerenone in participants with type 1 diabetes and chronic kidney disease: The FINE-ONE trial
Bibliographic record
Abstract
Aims Despite guideline-recommended treatments, including renin angiotensin system inhibition, up to 40 % of individuals with type 1 diabetes develop chronic kidney disease (CKD) putting them at risk of kidney failure. Finerenone is approved to reduce the risk of kidney failure in individuals with type 2 diabetes. We postulate that finerenone will demonstrate benefits on kidney outcomes in people with type 1 diabetes. Methods FINE-ONE (NCT05901831) is a randomised, placebo-controlled, double-blind phase III trial of 7.5 months' duration in ∼220 adults with type 1 diabetes, urine albumin/creatinine ratio (UACR) of ≥ 200–< 5000 mg/g (≥ 22.6–< 565 mg/mmol) and eGFR of ≥ 25–< 90 ml/min/1.73 m 2 . Results The primary endpoint is relative change in UACR from baseline over 6 months. UACR is used as a bridging biomarker (BB), since the treatment effect of finerenone on UACR was associated with its efficacy on kidney outcomes in the type 2 diabetes trials. Based on regulatory authority feedback, UACR can be used as a BB for kidney outcomes to support registration of finerenone in type 1 diabetes, provided necessary criteria are met. Secondary outcomes include incidences of treatment-emergent adverse events, treatment-emergent serious adverse events and hyperkalaemia. Conclusions FINE-ONE will evaluate the efficacy and safety of finerenone in type 1 diabetes and CKD. Finerenone could become the first registered treatment for CKD associated with type 1 diabetes in almost 30 years. Trial registration: ClinicalTrials.gov NCT05901831.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".