Intra‐individual variation of cystatin C and creatinine in pediatric solid organ transplant recipients
Bibliographic record
Abstract
There is controversy about the feasibility of cystatin C (CysC) as a marker of glomerular filtration rate (GFR) post-transplant (Tx). We studied intra-patient variability of CysC in comparison with serum creatinine (SCr) in 20 children (11 males, mean age 11.5 +/- 6.4 yr) with solid organ transplants (14 kidney, four liver, and two combined liver + kidney transplants). The mean age at Tx was 7.0 +/- 5.6 yr. A total of 178 simultaneous SCr and CysC measurements (median 8 per patient) were analyzed. In addition, GFR was calculated using the Schwartz and a novel CysC-based formula. Intra-individual coefficient of variations (CV) was calculated as ratio of standard deviation over mean. The mean CV was significantly lower for SCr (7.71 +/- 4.16%) when compared with CysC (10.27 +/- 4.87, p = 0.04), but was no longer significantly different when excluding patients with a bladder augment. The CV of the GFR estimated by Schwartz formula (7.44 +/- 3.77) was significantly lower than GFR calculated from CysC (12.52 +/- 7.37), p = 0.001. The mean ratio between the Schwartz GFR and the GFR calculated from CysC was 102.6 +/- 12.8%, not significantly different from 100% (p = 0.3796). The only potential confounding factors to explain increased CV after Tx were gender and bladder augmentation, whereas calcineurin inhibitors or steroids did not influence CV. With the limitation of a small number of subjects, our data suggest that the CysC and the CysC-calculated GFR is equivalent but not better than SCr and Schwartz formula. We therefore conclude that measurement of CysC can be used for longitudinal intra-individual follow-up of renal function 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 | 0.002 | 0.008 |
| 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 | 0.001 | 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 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".