Burden, Access, and Disparities in Kidney Disease
Bibliographic record
Abstract
Introduction Kidney disease is a global public health problem that affects more than 750 million persons worldwide.1 The burden of kidney disease varies substantially across the world, as does its detection and treatment. Although the magnitude and impact of kidney disease is better defined in developed countries, emerging evidence suggests that developing countries have a similar or even greater kidney disease burden.2 In many settings, rates of kidney disease and the provision of its care are defined by socioeconomic, cultural, and political factors, leading to significant disparities in disease burden, even in developed countries.3 These disparities exist across the spectrum of kidney disease-from preventive efforts to curb development of acute kidney injury (AKI) or chronic kidney disease (CKD), to screening for kidney disease among persons at high risk, to access to subspecialty care and treatment of kidney failure with renal replacement therapy (RRT). World Kidney Day 2019 offers an opportunity to raise awareness of kidney disease and highlight disparities in its burden and current state of global capacity for prevention and management. In this editorial, we highlight these disparities and emphasize the role of public policies and organizational structures in addressing them. We outline opportunities to improve our understanding of disparities in kidney disease, the best ways for them to be addressed, and how to streamline efforts toward achieving kidney health equity across the globe.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".