An examination of contemporary challenges in deceased donor kidney allocation
Bibliographic record
Abstract
Transplantation is the preferred treatment for patients with kidney failure, but the need for transplantation exceeds the organ supply. Strategies to address the organ shortage include: preventing end-organ failure, increasing the number of deceased donor kidneys (DDK) for transplantation, and ensuring appropriate allocation to avoid organ waste. This thesis: develops an improved metric for deceased donation activity; describes an age-matching allocation strategy to reduce organ waste; and examines the impact of selected wait-list policies on disparities in access to DDK transplantation. Using national administrative databases, we: Estimated and validated the number of potential deceased organ donors among in-hospital deaths using diagnostic codes; Calculated differences in DDK and transplant candidate survival by age using Cox regression to determine the area between survival curves (ABSC), and combined these measures with information on DDK and candidate ages to define age cut-points for DDK allocation; Described the use and outcomes of older DDKs (≥ 65 years) in countries with different allocation systems using Cox regression and ABSC, and identified patients that achieved a lifetime of transplant function from older DDKs; and Examined longitudinal use and outcomes of wait-listing candidates at multiple transplant centres using logistic and Cox regression. Three percent of Canadians who die in-hospital were identified as potential organ donors, suggesting significant potential to increase deceased donation. We determined DDK and candidate age cut-points for Canadian allocation, and estimated that implementation of these cut-points could have eliminated 500 years of wasted donor kidney function, and prevented 800 years of post-transplant dialysis compared to the current allocation strategy. We found that older DDKs provided a lifetime of kidney function for patients aged >60 years, suggesting targeted use of these organs could safely increase transplantation. Finally, we determined that multiple wait-listing helped minimize geographic disparities in accessing transplantation and may be an important policy consideration in countries that do not currently allow multiple listing. As transplantation wait-lists grow at unprecedented rates, the potential to increase deceased donation, implement allocation policies to decrease organ wastage, safely expand the use of older deceased donors and promulgate wait-list policies to increase access to transplantation will become more important.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".