Validation of the FINE-CKD model for future health technology assessments for finerenone in patients with chronic kidney disease and type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: The FINE-CKD model was developed to estimate the cost-effectiveness of finerenone in patients with chronic kidney disease (CKD) and type 2 diabetes (T2D). OBJECTIVE: To perform internal and external validation by comparing the model estimates with trial results and outcomes from other models. METHODS: Incidence rates from trials were compared with the model predictions. Statistical tests were then performed to assess whether modeled event rates aligned with trial observations. A cross-validation was also performed using the online version of the SHARP CKD-Cardiovascular Disease (SHARP CKD-CVD) model, with population characteristics from the finerenone trials analyzed. Where no finerenone data were available, the default SHARP CKD-CVD values were used. Comparison of the results considered the ranges from both models. RESULTS: The outcomes of the FINE-CKD model reflect the event rates observed in the trials. Based on the results of the statistical tests, the hypothesis of no difference between observed and modeled events cannot be rejected for any of the outcomes. The results of the FINE-CKD model are within the ranges from the SHARP CKD-CVD model. Disease progressions align across the models; however, incident kidney failure events in the SHARP CKD-CVD model were higher. This can be explained by simulation of more severely affected patients in the SHARP CKD-CVD model. CONCLUSIONS: This study demonstrates that the FINE-CKD model adequately reflects the clinical data and provides reliable extrapolation relative to the existing predictive tools while also being conservative in its approach.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".