Spectrum of chronic kidney disease in China: A national study based on hospitalized patients from 2010 to 2015
Bibliographic record
Abstract
AIM: To investigate the spectrum of chronic kidney disease (CKD) in China. METHODS: We used a large national in-patient database covering 878 class three hospitals and involving 64.7 million adult patients in China from 2010 to 2015. The class 3 hospital in China is ranked as the top tier of medical system in China with at least 500 beds and the accreditation from health authorities. The specific causes of CKD were extracted from the International Classification of Diseases-10 codes of discharge diagnoses. RESULTS: A total of 4.5% of hospitalized patients (1.8 million) were identified as CKD, with an increased percentage from 2010 (3.7%) to 2015 (4.7%). Increasing trends of diabetic kidney disease and hypertensive nephropathy were observed from 2010 to 2015 (19.5% vs 24.3% and 11.5% vs 15.9%, respectively), especially for urban residents from north China. The proportion of obstructive nephropathy also increased gradually (10.3% in 2010 vs 15.6% in 2015) and constituted another important cause of CKD for patients, especially for those from south China and rural residents. CONCLUSION: The spectrum of CKD is changing in China, with variations over time and geographic regions, which has implication regarding developing the prevention strategy of CKD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".