Pretransplant Use of the Chronic Kidney Disease Epidemiology Collaboration Equation (CKD-EPI) to Estimate Glomerular Filtration Rate Predicts Outcomes in Liver Transplant Recipients
Bibliographic record
Abstract
OBJECTIVES: Kidney dysfunction is common in liver transplant candidates and is a well-established predictor of increased mortality after liver transplant. However, the best method for determination of the glomerular filtration rate before liver transplant remains unclear. MATERIALS AND METHODS: We analyzed the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and the Modification of Diet in Renal Disease (MDRD) Study equation, before liver transplant, compared with radionuclide glomerular filtration rate and examined the association of the 2 equations with a composite outcome of stage 4 chronic kidney disease, initiation of chronic dialysis, or patient death. RESULTS: We studied 426 consecutive adult liver transplant recipients from 1990 to 2014. The correlation coefficient of the radionuclide glomerular filtration rate with the Chronic Kidney Disease Epidemiology Collaboration equation was 0.61 and with the Modification of Diet in Renal Disease Study equation was 0.58. The Modification of Diet in Renal Disease Study equation showed a bias of -4.7 mL/min and precision of 32.9 mL/min, whereas the Chronic Kidney Disease Epidemiology Collaboration equation showed a bias of -11.1 mL/min but was more precise (28.1 mL/min). Only the Chronic Kidney Disease Epidemiology Collaboration equation remained significantly associated with the composite outcome in the multivariable analysis. CONCLUSIONS: The use of the Chronic Kidney Disease Epidemiology Collaboration equation in the period before liver transplant provided independent prognostic information regarding long-term outcomes after liver transplant.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 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".