Change in estimated glomerular filtration rate and outcomes in chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Estimated glomerular filtration rate (eGFR) is important in the diagnosis and prognostication of chronic kidney disease (CKD). The current standards for CKD progression in clinical trials are kidney failure and the doubling of serum creatinine (∼57% decline in eGFR). These endpoints have limitations as they are only applicable to patients with later stages of CKD and often require large sample sizes to achieve adequate power. RECENT FINDINGS: Lesser declines in eGFR (30% and 40%) have been evaluated as potential endpoints in recent studies. These endpoints are more common and show a strong association with the risk of end-stage renal disease and mortality. These findings have been shown to be consistent across different causes of CKD and for different interventions. A particular limitation of reduced thresholds is an elevated risk of type I errors in the presence of acute treatment effects, particularly with a 30% eGFR decline cut off. SUMMARY: Surrogate endpoints for kidney failure and mortality are needed in clinical trials to allow for the reasonable management of timelines and resources, and the achievement of adequate sample sizes. Lesser eGFR decline thresholds should be considered to aid in the design and conduct of more randomized controlled trials in nephrology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".