Care for end-stage kidney disease in China: progress, challenges, and recommendations
Bibliographic record
Abstract
This review comprehensively evaluates China's progress in care of end-stage kidney disease (ESKD) by identifying achievements and gaps, reviewing ESKD-related policy initiatives, and proposing policy recommendations. In the past decade, China has made laudable progress in access to ESKD care with narrowed difference between the number of patients needing and receiving kidney replacement therapies (KRT). China has also experienced significant improvements in clinical quality and outcomes of ESKD care. These achievements stem from concerted efforts in advocating effective policies, increasing fiscal subsidies, re-designing health insurance schemes, encouraging healthcare delivery from both public and private sectors, and improving quality regulation. However, challenges remain, including inequitable access to care across regions and groups, and suboptimal quality and outcomes in some underdeveloped areas. To address these gaps, we recommend reforming the financing policy, adopting quality-based payment methods, strengthening quality monitoring system, improving chronic kidney disease prevention and management, and developing alternative KRT modalities.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".