REMODELing mechanistic trials for kidney disease: a multimodal, tissue-centered approach to understand the renal mechanism of action of semaglutide
Bibliographic record
Abstract
Chronic kidney disease poses a major global health burden and is one of the most common complications of type 2 diabetes. Drug trials have demonstrated that treatments, including sodium-glucose cotransporter-2 inhibitors, a glucagon-like peptide-1 receptor agonist, and a nonsteroidal mineralocorticoid receptor antagonist, mitigate chronic kidney disease progression, but the underlying nephroprotective mechanisms of action remain incompletely understood, partly ascribed to their pleiotropic actions. New innovative trial designs are needed to tackle simultaneously the hemodynamic and structural effects induced by these drugs. The REMODEL trial (REnal MODE of action of semagLutide in patients with type 2 diabetes and chronic kidney disease; ClinicalTrials.gov identifier: NCT04865770) is designed to explore the mechanisms of action of semaglutide with a novel multiparametric approach, integrating functional magnetic resonance imaging and research kidney biopsies for structural and molecular interrogation and blood and urine biomarker analysis. Better understanding of these mechanisms will help explain semaglutide's beneficial effects on kidney disease outcomes, reported in the FLOW (Evaluate Renal Function with Semaglutide Once Weekly; ClinicalTrials.gov identifier: NCT03819153) trial, and may identify patient subpopulations to optimize treatment strategies. By combining diverse and state-of-the-art methods, REMODEL aims to pave the way to a larger use of mechanism of action trials in the near future. To this purpose, this article describes the REMODEL framework, methods, technical challenges, and lessons learned as a platform for future mechanistic trials of chronic kidney disease therapies and beyond.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".