Kidney Function and Risk of Cardiovascular Disease and Mortality in Kidney Transplant Recipients: The FAVORIT Trial
Bibliographic record
Abstract
In kidney transplant recipients, cardiovascular disease (CVD) is the leading cause of death. The relationship of kidney function with CVD outcomes in transplant recipients remains uncertain. We performed a post hoc analysis of the Folic Acid for Vascular Outcome Reduction in Transplantation (FAVORIT) Trial to assess risk factors for CVD and mortality in kidney transplant recipients. Following adjustment for demographic, clinical and transplant characteristics, and traditional CVD risk factors, proportional hazards models were used to explore the association of estimated GFR with incident CVD and all-cause mortality. In 4016 participants, mean age was 52 years and 20% had prior CVD. Mean eGFR was 49 ± 18 mL/min/1.73 m(2) . In 3676 participants with complete data, there were 527 CVD events over a median of 3.8 years. Following adjustment, each 5 mL/min/1.73 m(2) higher eGFR at levels below 45 mL/min/1.73 m(2) was associated with a 15% lower risk of both CVD [HR = 0.85 (0.80, 0.90)] and death [HR = 0.85 (0.79, 0.90)], while there was no association between eGFR and outcomes at levels above 45 mL/min/1.73 m(2) . In conclusion, in stable kidney transplant recipients, lower eGFR is independently associated with adverse events, suggesting that reduced kidney function itself rather than preexisting comorbidity may lead to CVD.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".