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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".