Complete and Partial Remission as Surrogate End Points in Membranous Nephropathy
Bibliographic record
Abstract
Absent a remission of proteinuria, primary membranous nephropathy (MN) can lead to ESRD over many years. Therefore, use of an earlier end point could facilitate the conduct of clinical trials. This manuscript evaluates complete remission (CR) and partial remission (PR) of proteinuria as surrogate end points for a treatment effect on ESRD in patients with primary MN with heavy proteinuria. CR is associated with a low relapse rate and excellent long-term renal survival, and it plausibly reflects remission of the disease process that leads to ESRD. Patients who achieve PR have better renal outcomes than those who do not but may have elevated relapse rates. How long PR must be maintained to yield a benefit on renal outcomes is also unknown. Hence, available data suggest that CR could be used as a surrogate end point in primary MN, whereas PR seems reasonably likely to predict clinical benefit. In the United States, surrogate end points that are reasonably likely to predict clinical benefit can be used as a basis for accelerated approval; treatments approved under this program must verify the clinical benefit in postmarketing trials. Additional analyses of the relationship between treatment effects on CR and PR and subsequent renal outcomes would inform the design of future clinical trials in primary MN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".