Intraindividual variability of the modified Schwartz and novel CKiD GFR equations in pediatric renal transplant patients
Bibliographic record
Abstract
Tsampalieros A, Lepage N, Feber J. Intraindividual variability of the modified Schwartz and novel CKiD GFR equations in pediatric renal transplant patients. Pediatr Transplantation 2011: 15: 760–765. © 2011 John Wiley & Sons A/S. Abstract: GFR in children can be obtained from a formula using SCr and height or various formulas including serum CysC. Recently, two new GFR formulas have been developed: (i) height and SCr—mSchwartz GFR and (ii) height, SCr, CysC, and serum urea (CKiD GFR). While these formulas proved to be accurate when compared to the gold standard, their use in children post‐kidney Tx is yet to be assessed. A total of 1174 blood samples (urea, SCr and CysC) were analyzed from the post‐Tx period in 24 Tx children (12 boys, median age = 8.6 yr) currently followed at our institution. CKiD GFR and mSchwartz GFR were compared using Bland–Altman analysis and the CV. The mSchwartz GFR overestimated the CKiD GFR (mean bias = 1.09 ± 0.14; 95% limits of agreements from 0.82 to 1.36). Median CV of CKiD GFR (10.3%) was significantly lower than that of mSchwartz GFR (15.0%), p = 0.04, and negatively correlated with the slope of GFR (r2 = 0.34, p = 0.0026). In conclusion, CKiD GFR has a significantly lower intraindividual variation than mSchwartz GFR and may be better suited for longitudinal follow‐up of patients post‐Tx.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".