One Size Doesn't Fit All: A Review of International Deceased Donor Kidney Allocation Algorithms
Bibliographic record
Abstract
INTRODUCTION: Deceased donor kidney allocation algorithms seek to balance equity, need, and utility within regional healthcare constraints. Although many countries have formal systems, comparative analyses of their structure, context, and evolution remain scarce. SEARCH STRATEGY: We conducted a targeted review of 21 allocation algorithms across five continents, identified through literature searches, transplant organization websites, and policy documents. Core components analyzed included wait time, age, immunologic risk, medical urgency, and donor-recipient matching. Three case studies - the United States, Canada, and Eurotransplant - illustrate how governance and sociopolitical factors shape design and reform. RESULTS: All algorithms incorporated wait time and age, with variable definitions and weighting. Most addressed panel reactive antibody, pediatric priority, and medical urgency, but thresholds and implementation differed. Donor-recipient matching strategies included HLA mismatch scoring, ABO compatibility, and longevity matching via donor age, Kidney Donor Profile Index, or Expected Post-Transplant Survival. The US, Canadian, and Eurotransplant case studies highlighted contrasting centralized versus provincial governance and their influence on reform. CONCLUSIONS: Grounded in shared ethical principles, kidney allocation algorithms differ in how these are operationalized. This global comparison identifies opportunities to enhance transparency and equity, offering practical guidance for jurisdictions developing or refining allocation systems to align with ethical values and local realities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".