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 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 | 0.018 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".