Estimation of GFR in Patients With Cystic Fibrosis: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Patients with cystic fibrosis (CF) have frequent infectious complications requiring nephrotoxic medications, necessitating monitoring of renal function. Although adult studies have suggested that cystatin C (CysC)-based estimated glomerular filtration rate (eGFR) may be preferable due to reduced muscle mass of patients with CF, pediatric patients remain understudied. Objective: Our objective was to determine which eGFR formula is best for estimating glomerular filtration rate (GFR) in pediatric patients with CF. Methods: A total of 17 patients with CF treated with nephrotoxic antibiotics were recruited from the Children’s Hospital at London Health Sciences Centre, London, Ontario, Canada. 99 Tc DTPA GFR (measured GFR [mGFR]) was measured with 4-point measurements starting at 120 minutes using a 2-compartmental model with Brøchner-Mortensen correction, with simultaneous measurement of creatinine, urea, and CysC. The eGFR was calculated using 16 known equations based on creatinine, urea, CysC, or combinations of these. Primary outcome measures were correlation with mGFR, and agreement within 10% for various eGFR equations. Results: Mean mGFR was 136 ± 21 mL/min/1.73 m 2 . Mean creatinine, CysC, and urea were 38 ± 10 μmol/L, 0.72 ± 0.08 mg/L, and 3.9 ± 1.4 mmol/L, respectively. The 2014 Grubb CysC eGFR had the best correlation coefficient ( r = 0.75, P = .0004); however, only 35% were within 10%. The new Schwartz formula with creatinine and urea had the best agreement within 10%, but a relatively low correlation coefficient ( r = 0.63, P = .0065, 64% within 10%). Conclusions: Our study suggests that none of the eGFR formulae work well in this small cohort of pediatric patients with CF with preserved body composition, possibly due to inflammation causing false elevations of CysC. Based on the small numbers, we cannot conclude which eGFR formula is best.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".