Limitations of glomerular filtration rate equations in the renal transplant patient
Bibliographic record
Abstract
This study aims to compare the performance of endogenous creatinine clearance (CL(cr)) and a number of published mathematical equations to calculate glomerular filtration rate (GFR) in renal transplant patients considering (99m)Tc DTPA isotope scan as the reference method. A total of 152 GFR were performed on 81 renal transplant patients. Accuracy of each method was measured at different percentiles. The bias and precision of all the methods were then compared. A paired t-test was used to compare the performance of each calculation to the respective GFR measured by isotope study performed on the same day. In the total population, all calculated methods correlated significantly with the isotope results. Accuracies within specific ranges of the isotope GFR were limited in all equations (agreement with isotope result </=72% at 30% accuracy range in the total group). Within the limited accuracy, Edwards' equation (K.D. Edwards and H.M. Whyte, Australas Ann Med 1959; vol. 8: p. 218) had the least bias in the total population. Bjornsson (T.D. Bjornsson, Clin Pharmacokinet 1979; vol. 4: p. 200) had the least bias in patients with GFR >/= 50 mL/min and Gates in patients with GFR < 50 mL/min. Salazar (D.E. Salazar and G.B. Corcoran, Am J Med 1988; vol. 84: p. 1053) had the least bias in patients with BMI above 30 kg/m(2) and the Davis equation (G.A. Davis and M.H. Chandler, Am J Health Syst Pharm 1996; vol. 53: p. 1028) in patients with BMI <25 kg/m(2). In all analyses, Nankivell (B.J. Nankivell, S.M. Gruenwald, R.D.M. Allen and J.R. Chapman, Transplantation 1995; vol. 59: p. 1683) overestimated GFR by more than 80% and MDRD 1 and 2 in <10% of the time. The results demonstrate the inherited limitation in the currently available equations to calculate GFR in renal transplant patients.
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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 | 0.020 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".