CureGN-Diabetes Study: Rationale, Design, and Methods of a Prospective Observational Study of Glomerular Disease Patients with Diabetes
Bibliographic record
Abstract
Glomerular diseases (GDs) represent the third leading cause of end-stage kidney disease (ESKD) in the US Diabetes was excluded from the CureGN Study, an NIH/NIDDK-sponsored observational cohort study of four leading primary GDs: IgA nephropathy (IgAN), membranous nephropathy (MN), focal segmental glomerulosclerosis (FSGS), and minimal change disease (MCD). CureGN-Diabetes, an ancillary study to CureGN, seeks to understand how diabetes influences the diagnosis, treatment, and outcomes of GD. It is a multicenter, prospective cohort study, targeting an enrollment of 300 adults with prevalent type 1 or type 2 diabetes and MCD, FSGS, MN, or IgAN, with first kidney biopsy obtained within 5 years of enrollment in 80% (20% allowed if biopsy after 2010). CureGN and Transformative Research in DiabEtic NephropaThy (TRIDENT) provide comparator cohorts. Retrospective and prospective clinical data and patient-reported outcomes are obtained. Blood and urine specimens are collected at study visits annually. Kidney biopsy reports and digital images are obtained, and standardized pathologic evaluations performed. Light microscopy images are uploaded to the NIH pathology repository. Outcomes include relapse and remission rates, changes in proteinuria and estimated glomerular filtration rate, infections, cardiovascular events, malignancy, ESKD, and death. Multiple analytical approaches will be used leveraging the baseline and longitudinal data to compare disease presentation and progression across subgroups of interest. With 300 patients and an average of 3 years of follow-up, the study has 80% power to detect a HR of 1.4-1.8 for time to complete remission of proteinuria, a rate ratio for hospitalizations of 1.18-1.56 and difference in eGFR slope of 6.0-8.6 mL/min/year between two groups of 300 participants each. CureGN-Diabetes will enhance our understanding of diabetes as a modifying factor of the pathology and outcomes of GDs and support studies to identify disease mechanisms and improve patient outcomes in this understudied patient population.
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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 | 0.043 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".