Implementing the commitments of the WHO kidney health resolution: initial steps in 3 diverse settings
Bibliographic record
Abstract
Chronic kidney disease is a major noncommunicable disease that affects approximately 850 million people worldwide and is associated with a high burden of morbidity and mortality, especially in low- and middle-income countries. The recent resolution on kidney health at the 78th World Health Assembly creates an unprecedented opportunity for concerted action on kidney disease to accelerate global progress on noncommunicable disease prevention and control. We illustrate these opportunities using a case-study format with 3 selected countries-Guatemala, Thailand, and Somalia-which together demonstrate how the unique challenges associated with chronic kidney disease can be effectively addressed by targeted action at the country level. From these early efforts at implementing the resolution's commitments, 3 key lessons emerge for other countries to consider. First, developing a dedicated national strategy for kidney health is an essential step, which should be pursued together with integrating kidney health into other national policies for noncommunicable disease prevention and control. Second, strengthening capacity for the early detection and timely management of chronic kidney disease in primary care is arguably the highest kidney-related priority for all countries. Third, strengthening health data systems and surveillance infrastructure is critical for priority setting, resource allocation, addressing inequalities, assessing return on investment, and ensuring continuous quality improvement. In parallel with actions at the country level, the World Health Organization and other stakeholders, such as the International Society of Nephrology, can provide Member States with technical assistance and knowledge exchange related to essential medicines, key diagnostics, and kidney replacement. The International Society of Nephrology has commenced a multiyear strategy that will assist countries in implementing the resolution's commitments; the first phase will culminate in an implementation summit at the 2027 World Congress of Nephrology in Dubai. These actions will help to translate the commitments of the 78th World Health Assembly resolution into action, improving kidney health and health equity worldwide.
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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 | 0.097 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".