Global Estimates of Capacity for Kidney Transplantation in World Countries and Regions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it