Chronic kidney disease stage in renal transplantation classification using cystatin C and creatinine-based equations
Bibliographic record
Abstract
BACKGROUND: Current clinical guidelines recommend that renal transplant recipients (RTRs) be classified into chronic kidney disease (CKD) stage using a creatinine-based estimate of glomerular filtration rate (GFR). However, creatinine-based equations are inaccurate in RTRs leading to frequent CKD stage misclassification. It is not known whether the classification of CKD stage would be improved using a cystatin C-based estimate of GFR. METHODS: We measured (99m)Tc-DTPA GFR, cystatin C and creatinine in 198 stable RTRs. GFR was estimated using cystatin C-based equations (Filler, Le Bricon and Rule) and four creatinine-based equations. We determined the proportion, overall and by CKD stage, that were classified correctly by each equation as compared to the (99m)Tc-DTPA GFR. RESULTS: The Filler equation correctly classified 76% of patients compared to only 65% with the abbreviated modification of diet in renal disease (MDRD) equation and 69% with the Cockcroft-Gault equation. In CKD stages two and four, the Filler equation correctly classified 77% and 60% of patients whereas the abbreviated MDRD equation correctly classified 46% and 93% of patients. The area under the curve by receiver operating curve analysis for overall stage classification was uniformly poor for all equations (0.52-0.56). CONCLUSIONS: The cystatin C-based Filler and Le Bricon GFR estimates classified slightly more patients into the correct CKD stage than the standard creatinine-based equations in stable RTRs although the overall diagnostic accuracies were similar. The differences are modest and prospective studies will be needed to determine if the adoption of these equations for classification would lead to improved recognition of CKD complications or patient care.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".