Global kidney health priorities—perspectives from the ISN-GKHA
Bibliographic record
Abstract
Kidney diseases have become a global epidemic with significant public health impact. Chronic kidney disease (CKD) is set to become the fifth largest cause of death by 2040, with major impacts on low-resource countries. This review is based on a recent report of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) which uncovered gaps in key vehicles of kidney care delivery assessed using World Health Organization building blocks for health systems (financing, services delivery, workforce, access to essential medicines, health information systems and leadership/governance). High-income countries had more centres for kidney replacement therapies (KRT), higher KRT access, higher allocation of public funds to KRT, larger workforces, more health information systems, and higher government recognition of CKD and KRT as health priorities than low-income nations. Evidence identified from the current ISN-GKHA initiative should serve as template for generating and advancing policies and partnerships to address the global burden of kidney disease. The results provide opportunities for kidney health policymakers, nephrology leaders and organizations to initiate consultations to identify strategies for improving care delivery and access in equitable, resource-sensitive manners. Policies to increase use of public funding for kidney care, lower the cost of KRT and increase workforces should be a high priority in low-resource nations, while strategies that expand access to kidney care and maintain current status of care should be prioritized in high-income countries. In all countries, the perspectives of people with CKD should be exhaustively explored to identify core kidney care priorities.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".