Early change in proteinuria as a surrogate outcome in kidney disease progression: a systematic review of previous analyses and creation of a patient-level pooled dataset
Bibliographic record
Abstract
BACKGROUND: Proteinuria is a candidate surrogate end point for randomized controlled trials (RCTs) in chronic kidney disease (CKD). There is a reasonably sound biological basis for this hypothesis, but only preliminary empirical evidence currently exists. METHODS: A systematic review and creation of a patient-level dataset of randomized controlled trials (RCTs) in CKD that reported changes in proteinuria and assessed progression of kidney disease as defined by dialysis, transplantation, death, or changes in GFR or creatinine were performed. RESULTS: Systematic review. Seventy RCTs met the eligibility criteria; 17 eligible RCTs contained analyses of proteinuria as a predictor of outcomes; 15 RCTs concluded that greater proteinuria was associated with adverse outcomes. A majority were studies of diabetic or hypertensive kidney disease and tested renin-angiotensin system blockade. Definitions of predictor and outcome variables were too variable to conduct a meta-analysis of group data. Database creation. Over 4 years was required to create the patient-level dataset. The final dataset included 34 studies and > 9000 patients with a variety of CKD types and interventions. CONCLUSIONS: There are a relatively small number of RCTs designed to rigorously test therapies for kidney disease progression. Current analyses of change in proteinuria as a predictor of CKD progression are heterogeneous and incomplete, indicating further evaluation in a pooled individual patient-level database is necessary to advance knowledge in this field.
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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 | 0.054 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.020 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".