Value of serum cystatin <scp>C</scp> in estimating renal function in children with non‐renal solid organ transplantation
Bibliographic record
Abstract
Children with non-renal solid organ transplants are surviving longer, but outcome is complicated by CKD. Accurate and frequent renal function monitoring is imperative to recognize and institute measures early to reverse, prevent, or arrest progression. This study of 59 children determined the accuracy (P30), bias, sensitivity and specificity between measured renal function by NM-GFR, and estimated GFR by three formulas: Filler (serum cystatin C), mSchwartz (serum creatinine), and CKiD (serum cystatin C, creatinine, urea, and height). Mean GFR by all formulas differed significantly from NM-GFR. Filler and mSchwartz formulas significantly increased the proportion of patients with GFR ≥ 90 mL/min/1.73 m(2) (CKD stage 1) while decreasing those with GFR 60-89 mL/min/1.73 m(2) (CKD stage 2). All formulas overestimated GFR. CKiD showed the highest P30 and lowest bias (79.7%; 6.9 mL/min/1.73 m(2) ) followed by Filler (67.7%; 19.9 mL/min/1.73 m(2) ) and Schwartz (57.6%; 26.8 mL/min/1.73 m(2) ) for all GFR values. All formulas performed best with GFR ≥ 90 mL/min/1.73 m(2) , but CKiD was the only formula to achieve 91.1% accuracy. All formulas showed high sensitivities, but low specificities at NM-GFR cutoff at 90. Thus, GFR estimated by CKiD followed by Filler formula is an adequate method to monitor renal function closely and frequently in these children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".