Kidney Outcomes in IgAN Across the Age Spectrum in the Cure Glomerulopathy Network (CureGN)
Bibliographic record
Abstract
Background: There is sparse literature on IgAN outcomes across the age spectrum. Understanding disease trajectories by age at presentation is paramount. We report differences in histology and renal outcomes by age. Methods: CureGN is a prospective cohort study of adults and children with biopsy-proven glomerular disease. Descriptive statistics compared participant characteristics. Multivariable Cox proportional hazard models were fit to assess the relationship between age at enrollment and kidney failure (KF) outcomes. The effect of age on estimated glomerular filtration rate (eGFR) slope was evaluated using multivariable linear mixed models. Models were adjusted for race, eGFR, proteinuria, and time from biopsy to enrollment. Results: Included 653 participants with IgAN (enrollment age 6-12: n=59, 13-17: n=102, 18-44: n=309, 45-64: n=152 and 65+: n=31). Most were males (59%), white (80%) and non-Hispanic (85%). Median follow up time was 5.8 years (IQR: 1.7, 8.0). Age 18-44 yrs was the reference category. Hazard ratio (HR) for 40% decline in GFR or KF was highest in 13-17 yrs (HR 2.4; 95% confidence interval [CI] (1.13-5.13) and lowest in ages 65+ (0.35; 95% CI 0.13-0.98). Steepest eGFR decline was in 18-44 yrs (n=309) at -2.3 ml/min/1.73m2. Comparing MEST-C scores by age (n=286 patients with pathology), a significant difference in mesangial cellularity, segmental sclerosis and tubular atrophy was observed. Participants 18-44 yrs tended to have higher MEST-C scores. Conclusion: In CureGN, participants 13-44 yrs of age with IgAN had a high risk of eGFR decline and worse outcomes compared with other age groups therefore adolescence and young adulthood may be an important intervention window. Funding: NIDDK Support - NIDDK Support, NIDDK SupporteGFR slope by age at enrollment, HR of 40% decline or kidney failure and Radar plots of MEST-C score comparisons by age at biopsy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".