Long-term Kidney Transplant Survival Across the Globe
Bibliographic record
Abstract
BACKGROUND: The outcomes after kidney transplantation (KT), including access, wait time, and other issues around the globe, have been studied. However, issues do vary from one country to another. METHODS: We obtained data from several countries from North America, South America, Europe, Asia, and Australia, including the number of patients awaiting KT from 2015, transplant rate per million population (pmp), proportion of living donor and deceased donor (LD/DD) KT, and posttransplant survival. We also sought opinions on key difficulties faced by each of these countries with respect to KT and long-term survival. RESULTS: Variation in access to KT across the globe was noted. Countries with the highest rates of KT pmp included the United States (79%) and Spain (71%). A higher proportion of LD transplants was noted in Japan (93%), India (85%), Singapore (63%), and South Korea (63%). A higher proportion of DD KTs was noted in Spain (90%), Brazil (90%), France (85%), Italy (85%), Finland (85%), Australia-New Zealand (80%), and the United States (77%). The 5-y graft survival for LD was highest in South Korea (95%), Singapore (94%), Italy (93%), Finland (93%), and Japan (93%), whereas for DD, it was South Korea (93%), Italy (88%), Japan (86%), and Singapore (86%). The common issues surrounding KTs are access and a limited number of LDs and DDs. Key issues identified for long-term survival were increasing age of donors and recipients, higher recipient comorbidity, and posttransplant events, such as alloimmune injury to the kidney, infection, cancer, and suboptimal adherence to therapy. CONCLUSIONS: A unified approach is necessary to improve issues surrounding KT as the demand continues to increase.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".