Proteinuria Reduction as a Surrogate End Point in Trials of IgA Nephropathy
Bibliographic record
Abstract
IgA nephropathy (IgAN) is an important cause of ESKD for which there are no approved therapies. A challenge for evaluating treatments for IgAN is the usual long time course for progression to ESKD. The aim of this Kidney Health Initiative project was to identify surrogate end points that could serve as reliable predictors of a treatment's effect on long-term kidney outcomes in IgAN and be used as a basis for approval. Proteinuria was identified as the most widely recognized and well studied risk factor for progression to ESKD in IgAN. The workgroup performed a critical review of the data on proteinuria reduction as a surrogate end point for a treatment's effect on progression to ESKD in IgAN. Epidemiologic data indicate a strong and consistent relationship between the level and duration of proteinuria and loss of kidney function. Trial-level analyses of data from 13 controlled trials also show an association between treatment effects on percent reduction of proteinuria and treatment effects on a composite of time to doubling of serum creatinine, ESKD, or death. We conclude that data support the use of proteinuria reduction as a reasonably likely surrogate end point for a treatment's effect on progression to ESKD in IgAN. In the United States, reasonably likely surrogate end points can be used as a basis for accelerated approval of therapies intended to treat serious or life-threatening conditions, such as IgAN. The clinical benefit of products approved under this program would need to be verified in a postmarketing confirmatory trial.
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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 | 0.143 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| 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".