A Canadian Commentary on the NKF-ASN Task Force Recommendations on Reassessing the Inclusion of Race in Diagnosing Kidney Disease
Bibliographic record
Abstract
In 2021, a committee was commissioned by the Canadian Society of Nephrology to comment on the 2021 National Kidney Foundation-American Society of Nephrology Task Force recommendations on the use of race in glomerular filtration rate estimating equations. The committee met on numerous occasions and agreed on several recommendations. However, the committee did not achieve unanimity, with a minority group disagreeing with the scope of the commentary. As a result, this report presents the viewpoint of the majority members. We endorsed many of the recommendations from the National Kidney Foundation-American Society of Nephrology Task Force, most importantly that race should be removed from the estimated glomerular filtration rate creatinine-based equation. We recommend an immediate implementation of the new Chronic Kidney Disease Epidemiology Collaboration equation (2021), which does not discriminate among any group while maintaining precision. Additionally, we recommend that Canadian laboratories and provincial kidney organizations advocate for increased testing and access to cystatin C because the combination of cystatin C and creatinine in revised equations leads to more precise estimates. Finally, we recommend that future research studies evaluating the implementation of the new equations and changes to screening, diagnosis, and management across provincial health programs be prioritized in Canada.
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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 | 0.046 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.067 | 0.081 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".