Donor race and outcomes in kidney transplant recipients
Bibliographic record
Abstract
BACKGROUND: African Americans are at greater risk to reach end-stage renal disease and this risk may carry over in a kidney transplant recipient after kidney transplantation. METHODS: Linking the five-yr patient data of a large dialysis organization to the Scientific Registry of Transplant Recipients, we identified 13 692 hemodialysis patients who underwent first kidney transplantation. Mortality or graft failure and delayed graft function risks were estimated by Cox's regression (hazard ratio [HR] and 95% confidence interval) and logistic regression, respectively. RESULTS: Patients were 48 ± 14 yr old and included 39% women and 26% patients with diabetes. After adjusting for several relevant clinical and transplant-related variables, African American donor race was associated with higher all-cause mortality, with HR of 1.39 (1.09-1.78) for all-cause mortality, 1.80 (1.17-2.76) for cardiovascular mortality, 1.30 (1.03-1.64) for death-censored graft loss and 1.31 (1.10-1.57) for combined outcome over the six-yr observation period. In the non-African American recipient subcohort, but not in the African American recipient subcohort, African American donor race was associated with higher risk of death-censored graft loss (2.24 [1.44-3.49]) in our fully adjusted model. CONCLUSIONS: African American donor race was associated with increased all-cause and cardiovascular mortality and graft loss.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".