Limited Evidence for Use of a Black Race Modifier in eGFR Calculations: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Commonly used estimated glomerular filtration rate (eGFR) equations include a Black race modifier (BRM) that was incorporated during equation derivation. Race is a social construct, and a poorly characterized variable that is applied inconsistently in clinical settings. The BRM results in higher eGFR for any creatinine concentration, implying fundamental differences in creatinine production or excretion in Black individuals compared to other populations. Equations without inclusion of the BRM have the potential to detect kidney disease earlier in patients at the greatest risk of chronic kidney disease (CKD), but also has the potential to over-diagnose CKD or impact downstream clinical interventions. The purpose of this study was to use an evidence-based approach to systematically evaluate the literature relevant to the performance of the eGFR equations with and without the BRM and to examine the clinical impact of the use or removal. CONTENT: PubMed and Embase databases were searched for studies comparing measured GFR to eGFR in racially diverse adult populations using the Modification of Diet in Renal Disease or the 2009-Chronic Kidney Disease Epidemiology Collaboration-creatinine equations based on standardized creatinine measurements. Additionally, we searched for studies comparing clinical use of eGFR calculated with and without the BRM. Here, 8632 unique publications were identified; an additional 3 studies were added post hoc. In total, 96 studies were subjected to further analysis and 44 studies were used to make a final assessment. SUMMARY: There is limited published evidence to support the use of a BRM in eGFR equations.
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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 | 0.020 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".