eGFR slope modelling predicts long-term clinical benefit with nefecon in a real-world IgAN population
Bibliographic record
Abstract
Background: Nefecon is an oral, targeted-release formulation of budesonide approved to reduce kidney function loss in patients with immunoglobulin A nephropathy (IgAN). In the phase 3 NefIgArd trial, 9 months of nefecon treatment preserved estimated glomerular filtration rate (eGFR) and reduced urine protein-creatinine ratio versus placebo, for 15 months post-treatment. A modelling analysis was conducted to predict nefecon's long-term benefits on clinical outcomes. Methods: , or sustained doubling of serum creatinine. This model was applied to registry data from patients with IgAN at Leicester General Hospital (LGH), whose records were matched to individual NefIgArd patients on the basis of their urine protein-creatinine ratio and eGFR values. Results: A total of 1684 LGH-NeflgArd 'matched pairs' were obtained. Nefecon was predicted to delay the time to clinical outcome by 12.8 years (95% confidence interval 4.8-27.9), with median time to outcome of 9.6 years for patients receiving supportive care only versus 22.4 years for nefecon-treated patients. The NeflgArd 2-year eGFR slope yielded a log hazard ratio for the clinical outcome of 0.38 (95% confidence interval 0.21-0.63), a 62% risk reduction versus placebo. Of patients receiving only supportive care, 52% were modelled to have a clinical outcome within 10 years versus 24% of nefecon-treated patients. Conclusion: This modelling analysis indicates that the eGFR benefit seen with nefecon predicts a substantial delay in progression to kidney failure.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".