Early Changes in Kidney Function Predict Long-Term Chronic Kidney Disease and Mortality in Patients After Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a well-known complication after liver transplantation (LT) and is associated with increased mortality. The purpose of this study was to determine risk factors of advanced CKD and mortality after LT. METHODS: Four hundred forty-five adult patients underwent LT between June 1990 and September 2007 and survived more than 1 month. Multivariate Cox regression analyses were performed for time to CKD stage 4 (glomerular filtration rate [GFR] ≤30 mL/min), time to chronic dialysis, and all-cause mortality. Several patient and disease characteristics were used as independent pre- and posttransplant variables. We specifically analyzed a drop more than or equal to 30% in the estimated GFR (eGFR) during the first year posttransplant. RESULTS: Diabetes mellitus pretransplant and a drop more than or equal to 30% in the eGFR between 3 and 12 months predicted CKD stage 4 (odds ratio [OR] 4.1, 95% confidence interval [CI] 1.9-5.4, P<0.001 and OR 16.1, 95% CI 5.9-44.5, P<0.0001, respectively), the need for chronic dialysis (OR 3.8, 95% CI 1.1-13.2, P=0.03 and OR 14.6, 95% CI 3.0-71.4, P<0.001, respectively), and all-cause mortality (OR 1.9, 95% CI 1.2-2.9, P=0.004 and OR 2.6, 95% CI 1.6-4.4, P<0.001, respectively), more than 1 year after LT. CONCLUSIONS: Diabetes mellitus pretransplant and a drop more than or equal to 30% in the eGFR within the first year are strong predictors of advanced CKD, chronic dialysis, and death more than 1 year after LT. These easily determined clinical variables define a population at risk for CKD who should be targeted for renal protection strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".