Recipient race modifies the association between obesity and long-term graft outcomes after kidney transplantation
Bibliographic record
Abstract
) and combined donor and recipient (DR) obesity pairing, with death-censored graft loss (DCGL), all-cause graft loss (ACGL), and short-term graft outcomes using multivariable Cox proportional hazards models and logistic regression. Obesity was associated with a higher risk of DCGL in White (adjusted hazard ratio [aHR], 1.29; 95% CI, 1.25-1.35) than Black (aHR, 1.13; 95% CI, 1.08-1.19) recipients. White, but not Black, recipients with obesity were at higher risk for ACGL (aHR, 1.08; 95% CI, 1.05-1.11, for White recipients; aHR, 0.99; 95% CI, 0.95-1.02, for Black recipients). Relative to nonobese DR, White recipients with combined DR obesity experienced more DCGL (aHR, 1.38; 95% CI, 1.29-1.47 for White; aHR, 1.19; 95% CI, 1.10-1.29 for Black) and ACGL (aHR, 1.12; 95% CI, 1.07-1.17 for White; aHR, 1.00; 95% CI, 0.94-1.07 for Black) than Black recipients. Short-term obesity risk was similar irrespective of race. An elevated BMI differentially affects long-term outcomes in Black and White KT recipients; uniform BMI thresholds to define transplant eligibility are likely inappropriate.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".