Validation of the Virga GFR Equation in a Renal Transplant Population
Bibliographic record
Abstract
BACKGROUND: Virga and colleagues derived a glomerular filtration rate (GFR) equation which demonstrated a superior performance over Cockcroft-Gault (C-G) and modified diet in renal disease-isotope dilution mass spectrometry (MDRD-IDMS) formulas in chronic kidney disease (CKD) patients. AIM: To validate the performance of the Virga equation on 103 renal transplant patients. METHODS: We compared the performances of the MDRD-IDMS, C-G and Virga equations using inulin clearance as a reference test. Error, accuracy, relative accuracy, precision, scatter, and coefficient of variance of each equation were tested. RESULTS: The mean absolute percentage error in estimated GFR by the new equation was 39.8 +/- 36.34% (mean +/- SD). Relative accuracy at 10, 30 and 50% range were 18.44, 48.54 and 73.78%, respectively. It has a bias of 0.09 +/- 0.169 and a precision of 19.69. Inulin clearance (GFR) in stages 1-4 were 106.19 +/- 14.11, 71.17 +/- 7, 42.37 +/- 8.40 and 22.92 +/- 3.48 ml/min/1.73 m(2), respectively. Comparative statistics in the overall population and in patients with transplant CKD stage 3T showed that the MDRD-IDMS equation had better accuracy. The performance of MDRD-IDMS over the Virga equation was clearly superior for males. In patients with CKD stage 2T, the Virga equation showed superiority over MDRD-IDMS. In the overall and subpopulations, the Virga equation performed better than the C-G equation. CONCLUSION: Among renal transplant patients, the results suggest that the best GFR estimate is probably obtained using the MDRD-IDMS equation in moderate kidney failure whilst the Virga formula was superior to MDRD-IDMS for patients with mild kidney failure. As in untransplanted patients, estimating GFR with the MDRD-IDMS equation is not advisable in the range of normal renal function because of its known underestimation of renal function.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".