Global Estimates of Capacity for Kidney Transplantation in World Countries and Regions
Bibliographic record
Abstract
BACKGROUND: Kidney transplantation (KT) is the optimal treatment for kidney failure and is associated with better quality of life and survival relative to dialysis. However, knowledge of the current capacity of countries to deliver KT is limited. This study reports on findings from the 2018 International Society of Nephrology Global Kidney Health Atlas survey, specifically addressing the availability, accessibility, and quality of KT across countries and regions. METHODS: Data were collected from published online sources, and a survey was administered online to key stakeholders. All country-level data were analyzed by International Society of Nephrology region and World Bank income classification. RESULTS: Data were collected via a survey in 182 countries, of which 155 answered questions pertaining to KT. Of these, 74% stated that KT was available, with a median incidence of 14 per million population (range: 0.04-70) and median prevalence of 255 per million population (range: 3-693). Accessibility of KT varied widely; even within high-income countries, it was disproportionately lower for ethnic minorities. Universal health coverage of all KT treatment costs was available in 31%, and 57% had a KT registry. CONCLUSIONS: There are substantial variations in KT incidence, prevalence, availability, accessibility, and quality worldwide, with the lowest rates evident in low- and lower-middle income countries. Understanding these disparities will inform efforts to increase awareness and the adoption of practices that will ensure high-quality KT care is provided around the world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".