The Global Kidney Health Atlas: Burden and Opportunities to Improve Kidney Health Worldwide
Bibliographic record
Abstract
CKD is a growing public health problem. The Global Kidney Health Atlas (GKHA) is an important initiative of the International Society of Nephrology. The GKHA aims to improve the understanding of inter- and intranational variability across the globe, focusing on capacity for kidney care delivery. The GKHA survey was launched in 2017 and then again in 2019, using the same core data, supplemented by information about dialysis access and conservative care. Based on a WHO framework of the 6 building blocks essential for health care, the GKHA assesses capacity in 6 domains: information systems, services delivery, workforce, financing, access to essential medicines, and leadership/governance. In addition, the GKHA assesses the capacity for research in all regions of the world, across all domains (basic, translational, clinical, and health system research). The results of the GKHA have informed policy and been used to enhance advocacy strategies in different regions. In addition, through documentation of the disparities within and between countries and regions, initiatives have been launched to foster change. Since the first survey, there has been an increase in the number of countries which have registries to document the burden of CKD or dialysis. For many, information about the burden of disease is the first step toward addressing care delivery issues, including prevention, delay of progression, and access to services. Worldwide collaboration in the documentation of kidney health and disease is an important step toward the goal of ensuring equitable access to kidney health worldwide.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".