Disparities in Access to Preemptive Repeat Kidney Transplant: Still Missing the Mark?
Bibliographic record
Abstract
Background: The need for repeat transplant due to failing kidney allografts is increasing over time. The benefit of preemptive kidney retransplant (PKre-T) is controversial. Marginalized populations are less likely to undergo their first transplant preemptively; however, whether inequities exist for those undergoing PKre-T is unknown. Methods: We performed a cohort study of adult patients undergoing live and deceased kidney transplant in the United States from 2000 to 2018 identified using the Scientific Registry of Transplant Recipients, and we identified patients with first preemptive kidney transplant (PKT) and PKre-T. In the primary analysis, a multivariable logistic regression was used to identify independent predictors of PKre-T. In secondary analyses, multivariable Cox models were used to determine the association of PKre-T with death-censored and all-cause graft loss. Results: In total, 4910 (15.5%) patients underwent PKre-T, and 43,293 (19.1%) underwent first PKT. Inequities in access to PKre-T persisted (OR, 0.49; 95% CI, 0.44 to 0.55 for unemployed versus full time; OR, 1.61; 95% CI, 1.14 to 2.25 for graduate school versus not completing high school; OR, 0.61; 95% CI, 0.52 to 0.70 for Black versus White race); 7.1% of all transplanted Black patients received PKre-T versus 17.4% of White patients. Women were more likely to undergo PKre-T than men (OR, 1.42; 95% CI, 1.29 to 1.57). PKre-T was associated with superior graft survival relative to retransplant after a period of dialysis (HR, 0.73; 95% CI, 0.67 to 0.80 for all-cause graft failure; HR, 0.72; 95% CI, 0.65 to 0.81 for death-censored graft loss). Conclusions: Despite improved patient and graft survival, inequities in access to PKre-T persist. Patients with lower education, patients with reduced employment status, patients of Black race, and men are less likely to receive PKre-T.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".