Renal Dysfunction Is a Strong and Independent Risk Factor for Mortality and Cardiovascular Complications in Renal Transplantation
Bibliographic record
Abstract
Renal transplant recipients (RTR) have shortened life expectancy, primarily due to premature cardiovascular disease (CVD). Traditional CVD risk factors are highly prevalent. In addition, several non-traditional risk factors may contribute to the high risk. The aim of the study was to evaluate the effects of renal dysfunction on mortality and cardiovascular complications in 1052 placebo-treated patients of the Assessment of LEscol in Renal Transplantation (ALERT) trial. Follow-up was 5-6 years and endpoints included cardiac death, non-cardiovascular death, all-cause mortality, major adverse cardiac event (MACE), non-fatal myocardial infarction (MI) and stroke. The effects of serum creatinine at baseline on these endpoints were evaluated. Elevated serum creatinine in RTR was a strong and independent risk factor for MACE, cardiac, non-cardiovascular, and all-cause mortality, but not for stroke or non-fatal MI alone. Serum creatinine was associated with increased mortality and MACE, independent of established CVD risk factors. Graft loss resulted in increased incidences of non-cardiovascular death, all-cause mortality, MACE and non-fatal MI. In conclusion, elevated serum creatinine is a strong risk factor for all-cause, non-cardiovascular and cardiac mortality, and MACE, independent of traditional risk factors, but not for stroke or non-fatal MI alone.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".