Spectrum of chronic kidney disease in China: A national study based on hospitalized patients from 2010 to 2015
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it