Sustainable Development Goals: Challenges and the Role of the International Society of Nephrology in Improving Global Kidney Health
Bibliographic record
Abstract
The United Nations 2030 agenda for sustainable development includes 17 sustainable development goals (SDGs) that represent a universal call to end poverty and protect the planet, and are intended to guide government and private sector policies for international cooperation and optimal mobilization of resources. At the core of their achievement is reducing mortality by improving the global burden of noncommunicable diseases (NCDs), the leading causes of death and disability worldwide. CKD is the only NCD with a consistently rising age-adjusted mortality rate and is rising steadily up the list of the causes of lives lost globally. Kidney disease is strongly affected by social determinants of health, with a strong interplay between CKD incidence and progression and other NCDs and SDGs. Tackling the shared CKD and NCD risk factors will help with progress toward the SDGs and vice versa . Challenges to global kidney health include both preexisting socioeconomic factors and natural and human-induced disasters, many of which are intended to be addressed through actions proposed in the sustainable development agenda. Opportunities to address these challenges include public health policies focused on integrated kidney care, kidney disease surveillance, building strategic partnerships, building workforce capacity, harnessing technology and virtual platforms, advocacy/public awareness campaigns, translational and implementation research, and environmentally sustainable kidney care.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 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".