Renal Function, Albuminuria, and the Risk of Cardiovascular Events After Kidney Transplantation
Bibliographic record
Abstract
Background The risk of mortality and graft loss is higher in kidney transplant recipients with reduced estimated glomerular filtration rate (eGFR) and albuminuria. It is unclear whether these markers are also associated with cardiovascular events. Methods We examined linked healthcare databases in Alberta, Canada to identify kidney transplant recipients between 2002 and 2013 who had at least 1 outpatient serum creatinine and albuminuria measurement at 1-year posttransplant. We determined the relationship between categories of eGFR and albuminuria and the risk of subsequent cardiovascular events. Results Among 1069 eligible kidney transplant recipients, the median age was 52 years, 37% were female, and 52% had eGFR ≥60 mL/min per 1.73 m2. Over a median follow-up of 6 years, the adjusted rate of all-cause mortality and cardiovascular events was 2.7-fold higher for recipients with eGFR 15-29 mL/min per 1.73 m2 and heavy albuminuria compared to recipients with eGFR ≥60 mL/min per 1.73 m2 and normal albuminuria (rate ratio, 2.7; 95% confidence interval, 1.3-5.7). Similarly, recipients with heavy albuminuria had a threefold increased risk of all-cause mortality and heart failure compared with recipients with eGFR ≥60 mL/min per 1.73 m2 and normal albuminuria. Conclusions These findings suggest that eGFR and albuminuria should be used together to determine the risk of cardiovascular outcomes in transplant recipients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".