FINE-CKD model to evaluate economic value of finerenone in patients with chronic kidney disease and type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a progressive and irreversible disease often associated with type 2 diabetes (T2D). CKD is associated with an elevated risk of cardiovascular (CV) events, increased mortality, and diminished quality of life. Finerenone is a new treatment for patients with CKD and T2D that delays CKD progression and reduces CV complications. OBJECTIVE: To describe the approach and structure of a costeffectiveness model for finerenone for patients with CKD and T2D and compare it with existing economic models in CKD. METHODS: A de novo cost-effectiveness model (FINE-CKD model), reflective of FIDELIO-DKD results, was developed for finerenone. The FINE-CKD model was designed and implemented in accordance with published guidance on modeling and was developed with input from economic and clinical experts. The final model approach was evaluated against existing modeling structures in CKD identified through a systematic literature review. RESULTS AND CONCLUSIONS: The FINE-CKD model structure follows recommended modeling guidelines and has been designed in accordance with the best practices of modeling in CKD, while also incorporating important features of the FIDELIO-DKD design and results. The approach is consistent with the published literature, ensuring transparency and minimizing uncertainty that can arise from unnecessary complexity. The FINE-CKD model allows for reliable assessment of benefits and costs related to the use of finerenone in patients with CKD and T2D, and it is a reliable assessment of cost-effectiveness.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".