International comparison of trends in patients commencing renal replacement therapy by primary renal disease
Bibliographic record
Abstract
AIM: To examine international time trends in the incidence of renal replacement therapy (RRT) for end-stage renal disease (ESRD) by primary renal disease (PRD). METHODS: Renal registries reporting on patients starting RRT per million population for ESRD by PRD from 2005 to 2014, were identified by internet search and literature review. The average annual percentage change (AAPC) with a 95% confidence interval (CI) of the time trends was computed using Joinpoint regression. RESULTS: There was a significant decrease in the incidence of RRT for ESRD due to diabetes mellitus (DM) in Europe (AAPC = -0.9; 95%CI -1.3; -0.5) and to hypertension/renal vascular disease (HT/RVD) in Australia (AAPC = -1.8; 95%CI -3.3; -0.3), Canada (AAPC = -2.9; 95%CI -4.4; -1.5) and Europe (AAPC = -1.1; 95%CI -2.1; -0.0). A decrease or stabilization was observed for glomerulonephritis in all regions and for autosomal dominant polycystic kidney disease (ADPKD) in all regions except for Malaysia and the Republic of Korea. An increase of 5.2-16.3% was observed for DM, HT/RVD and ADPKD in Malaysia and the Republic of Korea. CONCLUSION: Large international differences exist in the trends in incidence of RRT by primary renal disease. Mapping of these international trends is the first step in defining the causes and successful preventative measures 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".