International Society of Nephrology Global Kidney Health Atlas: structures, organization, and services for the management of kidney failure in Western Europe
Bibliographic record
Abstract
Populations in the high-income countries of Western Europe are aging due to increased life expectancy. As the prevalence of diabetes and obesity has increased, so has the burden of kidney failure. To determine the global capacity for kidney replacement therapy and conservative kidney management, the International Society of Nephrology conducted multinational, cross-sectional surveys and published the findings in the International Society of Nephrology Global Kidney Health Atlas. In the second iteration of the International Society of Nephrology Global Kidney Health Atlas, we aimed to describe the availability, accessibility, quality, and affordability of kidney failure care in Western Europe. Among the 29 countries in Western Europe, 21 (72.4%) responded, representing 99% of the region's population. The burden of kidney failure prevalence varied widely, ranging from 760 per million population (pmp) in Iceland to 1612 pmp in Portugal. Coverage of kidney replacement therapy from public funding was nearly universal, with the exceptions of Germany and Liechtenstein where part of the costs was covered by mandatory insurance. Fourteen (67%) of 21 countries charged no fees at the point of care delivery, but in 5 countries (24%), patients do pay some out-of-pocket costs. Long-term dialysis services (both hemodialysis and peritoneal dialysis) were available in all countries in the region, and kidney transplantation services were available in 19 (90%) countries. The incidence of kidney transplantation varied widely between countries from 12 pmp in Luxembourg to 70.45 pmp in Spain. Conservative kidney care was available in 18 (90%) of 21 countries. The median number of nephrologists was 22.9 pmp (range: 9.47-55.75 pmp). These data highlight the uniform capacity of Western Europe to provide kidney failure care, but also the scope for improvement in disease prevention and management, as exemplified by the variability in disease burden and transplantation rates.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".