Hyperfiltration Affects Accuracy of Creatinine eGFR Measurement
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Surrogate markers such as creatinine, cystatin C (CysC), and beta trace protein (BTP) have been used to estimate GFR (eGFR). The accuracy of eGFR may be altered with hyperfiltration and differences in filtration fraction (FF). It is hypothesized that the accuracy of creatinine for eGFR may be affected by hyperfiltration and different effective renal plasma flow (ERPF). DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A total of 127 pediatric patients with various renal diseases underwent simultaneous measurements of GFR using 51Cr-EDTA renal scan and ERPF (131I-hippurate clearance) to calculate the FF (FF=GFR/ERPF). The eGFRs were calculated using the commonly used Schwartz (creatinine), Filler (CysC), and Benlamri (BTP) formulas. Agreement of the eGFRs with the measured isotope GFRs was assessed by Bland-Altman plots. Correlation analysis was performed using nonparametric tests to compare FF with eGFR-GFR. RESULTS: The 127 children at a median age (with 25th percentile, 75th percentile) of 11.9 (8.5, 14.9) years had a mean 51Cr EDTA-GFR of 100.6±32.1 ml/min per 1.73 m2 and a median 131I-hippurate clearance (ERPF) of 588 (398,739) ml/min per 1.73 m2. Mean FF was 17.7±4.5% with no correlation between the FF and the error (eGFR-GFR) for CysC and BTP eGFR, whereas there was a significant negative correlation between the error for Schwartz eGFR and FF. CONCLUSIONS: There is a significant negative correlation between the error for the Schwartz eGFR and the FF. CysC and BTP are not affected by differences in FF.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".