Effect of Adopting the New Race-Free 2021 Chronic Kidney Disease Epidemiology Collaboration Estimated Glomerular Filtration Rate Creatinine Equation on Racial Differences in Kidney Disease Progression among People with Human Immunodeficiency Virus: An Observational Study
Bibliographic record
Abstract
Background: The impact of adopting a race-free estimated glomerular filtration rate (eGFR) creatinine (eGFRcr) equation on racial differences in chronic kidney disease (CKD) progression among people with human immunodeficiency virus (PWH) is unknown. Methods: We defined eGFR stages using the original race-adjusted Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) eGFRcr equation and the new race-free CKD-EPI eGFRcr equation. We then estimated 5-year probabilities of transitioning from baseline kidney function to more advanced eGFR stages and examined the association of race (black vs white) with rates of CKD progression using Markov models. Results: With the race-adjusted eGFRcr equation, black participants (n = 31 298) had a lower risk of progressing from eGFR stage 1 to 2 (hazard ratio [HR], 0.77; 95% confidence interval [CI],. 73-.82), an equal risk of progressing from stage 2 to 3 (1.00;. 92-.07) and a 3-fold risk of progressing from stage 3 to 4 or 5 (3.06; 2.60-3.62), compared with white participants (n = 27 542). When we used the race-free eGFRcr equation, 16% of black participants were reclassified into a more severe eGFR stage at baseline. The reclassified black individuals had a higher prevalence of CKD risk factors than black PWH who were not reclassified. With the race-free eGFRcr equation, black participants had a higher risk of disease progression across all eGFR stages than white participants. Conclusions: The original eGFRcr equation systematically masked a subgroup of black PWH who are at high-risk of CKD progression. The new race-free eGFRcr equation unmasks these individuals and may allow for earlier detection and management of 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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".