Navigating the Global Economic Landscape of Dialysis: A Summary of Expert Opinions from The 4th International Congress of Chinese Nephrologists
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) continues to be a significant global public health issue. The escalating burden of CKD is probably driven by the aging population and the rising prevalence of diabetes. CKD not only adversely impacts an individual's health and well-being, but also poses significant challenge on the economy of the society. Summary: Experts from ten countries and regions around the world (Australia, Canada, China, Hong Kong, Malaysia, New Zealand, Singapore, Taiwan, United Kingdom, and United States) convened in the 4th International Congress of Chinese Nephrologists on December 1, 2023 to discuss the global dialysis burden. Although the cost of kidney replacement therapy (KRT) accounts for 2-3% of total healthcare expenditure in developed countries, patients with end stage kidney disease (ESKD) only represent a small percentage (<0.5%) of the population. Importantly, the economic impact of ESKD is not limited to direct medical costs, but extends to indirect societal costs, such as productivity loss by patients and caregivers. Primary prevention of CKD, early screening and treatment to delay progression to ESKD (where treatment costs rise dramatically), and utilization of home-based dialysis therapy (including peritoneal dialysis and home hemodialysis) shall be implemented as part of cost-containment strategy. Kidney transplant provides better outcomes than dialysis and is cost-effective in long run, whereas conservative kidney management should be considered for elderly frail patients. Key Messages: Implementation of preventive measures and cost-effective treatment strategies are the cornerstone to combat the global CKD epidemic.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".