AACC/NKF Guidance Document on Improving Equity in Chronic Kidney Disease Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it