AACC/NKF Guidance Document on Improving Equity in Chronic Kidney Disease Care
Bibliographic record
Abstract
BACKGROUND: Kidney disease (KD) is an important health equity issue with Black, Hispanic, and socioeconomically disadvantaged individuals experiencing a disproportionate disease burden. Prior to 2021, the commonly used estimated glomerular filtration rate (eGFR) equations incorporated coefficients for Black race that conferred higher GFR estimates for Black individuals compared to non-Black individuals of the same sex, age, and blood creatinine concentration. With a recognition that race does not delineate distinct biological categories, a joint task force of the National Kidney Foundation and the American Society of Nephrology recommended the adoption of the CKD-EPI 2021 race-agnostic equations. CONTENT: This document provides guidance on implementation of the CKD-EPI 2021 equations. It describes recommendations for KD biomarker testing, and opportunities for collaboration between clinical laboratories and providers to improve KD detection in high-risk populations. Further, the document provides guidance on the use of cystatin C, and eGFR reporting and interpretation in gender-diverse populations. SUMMARY: Implementation of the CKD-EPI 2021 eGFR equations represents progress toward health equity in the management of KD. Ongoing efforts by multidisciplinary teams, including clinical laboratorians, should focus on improved disease detection in clinically and socially high-risk populations. Routine use of cystatin C is recommended to improve the accuracy of eGFR, particularly in patients whose blood creatinine concentrations are confounded by processes other than glomerular filtration. When managing gender-diverse individuals, eGFR should be calculated and reported with both male and female coefficients. Gender-diverse individuals can benefit from a more holistic management approach, particularly at important clinical decision points.
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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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.031 | 0.013 |
| Insufficient payload (model declined to judge) | 0.050 | 0.023 |
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".