Diagnostic Accuracy of Cystatin C–Based eGFR Equations at Different GFR Levels in Children
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The diagnostic accuracy of cystatin C estimated GFR (eGFR) by various cystatin C equations have varied in different studies. We hypothesized that the GFR level of enrolled patients affects the diagnostic accuracy of a cystatin C equation. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We analyzed 240 consecutively enrolled children at a single Canadian center in a prospective and cross-sectional study. Cystatin C was analyzed with nephelometry, and cystatin C eGFR was estimated by the equations validated in children. GFR was measured by technetium-99m-diethylene-triamine penta-acetic acid (⁹⁹m)Tc DTPA). RESULTS: We compared various cystatin C equations across GFR strata < 60, < 90, ≥ 135, and ≥ 150 ml/min per 1.73 m² for an accurate prediction and appropriate classification of the measured GFR. The CKiD, Zappitelli-CysEq, and Zappitelli-CysCrEq equations had a higher accuracy, estimated by eGFR values within 10% and 30% of the respective (99m)Tc DTPA, in the GFR categories < 60 and < 90 ml/min per 1.73 m², whereas the Bökenkamp, Bouvet, and Filler equations had a greater accuracy in the GFR categories ≥ 135 and ≥ 150 ml/min per 1.73 m². The Bouvet, CKiD, Filler, Zappitelli-CysEq, and Zappitelli-CysCrEq equations had a greater sensitivity to classify GFR < 60 and < 90 ml/min per 1.73 m², whereas the Bökenkamp equation had a higher sensitivity for GFR ≥ 135 and ≥ 150 ml/min per 1.73 m². CONCLUSIONS: The diagnostic accuracy of various cystatin C equations varies with GFR. This issue needs consideration while applying these equations in clinical practice and for further research on eGFR equations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".