Comparison of Plasma Clearance With Early-Compartment Correction Equations and Urinary Clearance in High GFR Ranges
Bibliographic record
Abstract
Introduction Glomerular filtration rate (GFR) is measured from the late plasma disappearance curve of an exogenous tracer, after correction for the early decay—corresponding to the distribution of the tracer—using various equations. These equations display the highest discrepancies in the GFR range above 90 ml/min per 1.73 m 2 , and their respective performances against a reference, urinary GFR measurement are unclear. Methods In patients with mGFR >90 ml/min per 1.73 m 2 from 6 different cohorts, we compared GFR obtained from the plasma clearance of iohexol or 51 Cr-ethylenediamine tetraacetic acid (EDTA), after correction using Chantler (C), Bröchner-Mortensen (BM), Fleming (F), Jodal-Bröchner-Mortensen (JBM), and Ng (N) equations, with urinary clearance of the same tracers or inulin. Results In 438 participants (median age 41 [39–42] years, 43% women), the median urinary clearance was 100.8 (94.7–112.6) ml/min per 1.73 m 2 . Plasma clearances using the correction equations were 105.7 (96.8–119.2), 102.4 (95.2–112.9), 100.7 (93.6–111.1), 102.6 (95.2–113.4), and 106.0 (98.2–117.6) ml/min per 1.73 m 2 for C, BM, F, JBM, and N, respectively. Concordance correlation coefficients between plasma and urinary clearances were poor for all equations. Compared with urinary clearances, BM, F, and JBM displayed the best accuracy within 10% (73%, 72%, and 71%, respectively, vs. 63% and 66% for C and N), whereas BM and JBM had the lowest median biases. Accuracy of all equations was especially low in the hyperfiltration range (urinary clearance >130 ml/min per 1.73 m 2 ). Conclusion The BM and JBM equations displayed the best overall performances to correct for the early disappearance curve. Results of these equations should be interpreted with caution, especially in the highest GFR range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".