Global structures, practices, and tools for provision of hemodialysis
Bibliographic record
Abstract
BACKGROUND: Hemodialysis (HD) is the most commonly utilized modality for kidney replacement therapy worldwide. This study assesses the organizational structures, availability, accessibility, affordability and quality of HD care worldwide. METHODS: This cross-sectional study relied on desk research data as well as survey data from stakeholders (clinicians, policymakers and patient advocates) from countries affiliated with the International Society of Nephrology from July to September 2022. RESULTS: Overall, 167 countries or jurisdictions participated in the survey. In-center HD was available in 98% of countries with a median global prevalence of 322.7 [interquartile range (IQR) 76.3-648.8] per million population (pmp), ranging from 12.2 (IQR 3.9-103.0) pmp in Africa to 1575 (IQR 282.2-2106.8) pmp in North and East Asia. Overall, home HD was available in 30% of countries, mostly in countries of Western Europe (82%). In 74% of countries, more than half of people with kidney failure were able to access HD. HD centers increased with increasing country income levels from 0.31 pmp in low-income countries to 9.31 pmp in high-income countries. Overall, the annual cost of in-center HD was US$19 380.3 (IQR 11 817.6-38 005.4), and was highest in North America and the Caribbean (US$39 825.9) and lowest in South Asia (US$4310.2). In 19% of countries, HD services could not be accessed by children. CONCLUSIONS: This study shows significant variations that have remained consistent over the years in availability, access and affordability of HD across countries with severe limitations in lower-resourced countries.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".