Accuracy of cystatin C-based estimates of glomerular filtration rate in kidney transplant recipients: a systematic review
Bibliographic record
Abstract
BACKGROUND: As with creatinine, cystatin C can be incorporated into a formula to estimate the glomerular filtration rate (GFR). The overall performance of cystatin C-based equations in kidney transplantation is unclear with conflicting results between studies. METHODS: Systematic review of adult kidney transplant recipients. Studies that reported mean bias (mean difference between the measured and estimated GFRs) or accuracy of the cystatin C-based GFR estimation equation (e.g. percentage of estimates within 30% of the measured GFR) against the measured GFR using renal or plasma clearance of contrast agents, radioisotopes or inulin were included. RESULTS: The search identified 10 studies that examined 14 different cystatin C-based estimating equations (n = 5 equations evaluated in more than one study). The Le Bricon equation had the best performance with a bias that ranged from -6.4 to +2.8 mL/min/1.73 m(2); 85% (95% CI, 82-88) of estimates were within 30% of the measured GFR. For the other equations, 66-82% of estimates were within 30% of the measured GFR. For the modification of diet in renal disease (MDRD) equation, 68% (95% CI, 65-72) of estimates were within 30% of the measured GFR. CONCLUSIONS: The cystatin C-based Le Bricon equation was the most accurate, and most of the cystatin C-based equations showed improvements in 30% and 50% accuracy compared with the creatinine-based MDRD equation. Cystatin C-based equations may offer an advantage over the MDRD equation in kidney transplant recipients. Estimating equations re-expressed with standardized cystatin C have been developed and their accuracy needs to be tested in the kidney transplant population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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".