Switching to the CKD-EPI but Not Modified FAS eGFR Formula Underdetects CKD Among Adolescents and Young Adults in México
Bibliographic record
Abstract
Background: Guidelines recommend switching the glomerular filtration rate (eGFR) estimation from the CKiD-U25 to the CKD-EPI formula at age 18. We investigated how this would affect chronic kidney disease (CKD) classification. Methods: Serum creatinine was enzymatically measured in 1061 samples from 914 community-based 10–23-year-olds from Tlaxcala, Mexico, a region where urinary biomarkers demonstrated early kidney damage associated with exposure to inorganic toxins in a pediatric population. We calculated their eGFR using CKiD-U25, modified Schwartz, the first and modified Pottel full-age spectrum (FAS), and CKD-EPI formulae. Correlation analysis characterized the CKD stage stratified by age and sex. Results: At baseline, the median age was 13 (IQR: 12, 15) years, and 55% were female. Median CKiD-U25 eGFR was 96.9 (IQR: 83.3, 113.3) mL/min/1.73 m2, significantly lower than the CKD-EPI eGFR, which was 140.8 (IQR: 129.9, 149.3) mL/min/1.73 m2 (p < 0.0001, Wilcoxon rank test). The mean bias was 36.99 ± 12.89 mL/min/1.73 m2. Pearson correlation was r = 0.8296 (95% confidence interval 0.0898–0.8474). There was a better correlation between the modified Schwartz (r = 0.9421 (0.9349, 0.9485)) and the Pottel FAS (r = 0.9299 (0.9212, 0.9376)) formulae. Agreement was deficient when the eGFR was >75 mL/min/1.73 m2 in younger age and female sex. Modified Schwartz identified 281 (26.4%) measurements as having CKD 2 and 3 (2+), U25 identified 401 (37.7%) measurements as having CKD 2+, FAS identified 267 (25.1%) and modified FAS identified 282 (30%) measurements as having CKD 2+, and CKD-EPI identified 51 (4.8%) measurements as having CKD 2+, respectively. Conclusions: In this population, there needed to be better agreement between the various eGFR formulae. CKD-EPI identifies substantially fewer at-risk participants as having CKD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".