Post‐liver transplantation chronic kidney disease is associated with increased cardiovascular disease risk and poor survival
Bibliographic record
Abstract
Chronic kidney disease (CKD) is common following liver transplantation (LT). We aimed to investigate the frequency, risk factors, and impact of CKD on cardiovascular disease (CVD), graft, and patient survival. We analyzed 752 patients who received LT at the University of Alberta. Development of CKD was defined as eGFR <60 ml/min for greater than 3 months, intrinsic renal disease or presence of end-stage renal disease requiring renal replacement therapy. 240 patients were female (32%), and mean age at LT was 53 ± 11 years. CKD was diagnosed in 448 (60%) patients. On multivariable analysis, age (OR 1.3; P = 0.01), female sex (OR 3.3; P < 0.001), baseline eGFR (OR 0.83; P < 0.001), MELD (OR 1.03; P = 0.01), de novo metabolic syndrome (OR 2.3; P = 0.001), and acute kidney injury (OR 3.5; P < 0.001) were associated with CKD. A higher tacrolimus concentration to dose ratio was protective for CKD (OR 0.69; P < 0.001). CKD was associated with post-transplant CVD (26% vs. 16% P < 0.001), reduced graft (HR 1.4; P = 0.02), and patient survival (HR 1.3; P = 0.03). CKD is a frequent complication following LT and is associated with an increased risk of CVD and reduced graft and patient survival.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".