The Interplay Between Race and Poverty in Access to Transplantation.
Bibliographic record
Abstract
Disparities in transplantation are influenced by a complex interaction of factors. Most analyses have not considered the multiplicative impact of different sociodemographic factors. In this analysis we are examining the interaction of race and poverty in access to the waiting list, as well as deceased and living donor kidney transplantation (LDKT). Methods: Using data from USRDS and the US Census we identified (n= 1,119,193) adult incident ESRD patients without overt contraindications to transplantation between 1995 and 2007 and used Cox multivariate regression to determine the adjusted likelihood of waitlisting, deceased donor and LDKT in subgroups defined by race and quintile of median household income (Q1-Q5). Results: Race was consistently associated with access to the wait-list and both living and deceased donor transplantation (Figure). Even among wealthy patients that were actively wait-listed, black patients were less likely to undergo transplantation. In contrast, income had little effect on deceased donor transplantation among wait-listed patients (middle panel in Figure) and was most strongly associated with access to LDKT. The additive impact of race and income was most dramatic for access to living donor transplantation (bottom panel in Figure) with white high income ESRD patients having a 6.2 fold higher likelihood of LDKT compared to black low income patients. Conclusions: These data highlight the complex relationship between race and income and demonstrate the multiplicative impact of these factors on access to transplantation, particularly LDKT. When developing strategies to improve access to transplantation, racial and socioeconomic barriers need to be jointly considered, taking into account the multiplicative impact of these factors.Figure: No Caption available.The Figure outlines the likelihood of wait-listing, deceased donor transplantation after wait listing, and LDTX by race and median household income quintile.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".