Estimating Glomerular Filtration Rate in Kidney Transplantation: Is the New Chronic Kidney Disease Epidemiology Collaboration Equation Any Better?
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was developed to address the systematic underestimation of the glomerular filtration rate (GFR) by the Modification of Diet in Renal Disease (MDRD) Study equation in patients with a relatively well-preserved kidney function. The performance of the new equation for kidney transplant recipients (KTRs) is unknown. METHODS: We used the plasma clearance of (99m)Tc-diethylenetriamine pentaacetic acid to measure the GFR in a cohort of 207 stable KTRs and estimated the GFR with the new CKD-EPI equation. RESULTS: The mean bias for the CKD-EPI equation of -4.5 mL x min(-1) x (1.73 m(2))(-1) was lower than that of the 4-variable MDRD Study equation; however, the 2 equations showed similar variation of individual biases around the mean or median bias, so that only modest improvement was seen in the overall percentage of GFR estimates within 30% of the measured GFR (84% vs 77% for the CKD-EPI vs MDRD Study equations, respectively). In the cohort with a GFR >60 mL x min(-1) x (1.73 m(2))(-1) (n = 98), the CKD-EPI bias was much less than that of the MDRD Study equation [-7.4 mL x min(-1) x (1.73 m(2))(-1) vs -14.3 mL x min(-1) x (1.73 m(2))(-1)], and an accuracy of + or - 30% was seen for 89% of GFR estimates, compared with 77% with the MDRD Study equation. The variation of the individual biases around the mean bias remained substantial [SD = 13.7 mL x min(-1) x (1.73 m(2))(-1)]. CONCLUSIONS: The CKD-EPI equation shows improved estimation ability, and we recommend that it replace the MDRD Study equation as the currently preferred creatinine-based estimating equation for KTRs. The precision of GFR estimates obtained with the CKD-EPI equation remains suboptimal, however, and we recommend that research on other markers of GFR, such as cystatin C and beta-trace protein, be pursued.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it