Regional variation of underlying kidney diseases in children undergoing chronic kidney replacement therapy around the globe
Bibliographic record
Abstract
BACKGROUND: There is a scarcity of information regarding the distribution of the diseases leading to kidney failure (KF) in children living in the emerging world. We used registry data to provide a global overview of the underlying disease spectrum in children commencing kidney replacement therapy (KRT). METHODS: We analyzed KF causes among 23,620 children and adolescents commencing maintenance KRT in 80 countries, using data from the IPNA Global KRT Registry (including ESPN/ERA Registry), the International Pediatric Dialysis Network (IPDN), the United States Renal Data System (USRDS), and the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA). The analysis considered geographic region, country-level gross national income (GNI), average annual temperature, and patient age. RESULTS: Marked regional differences were observed in the distribution of KF causes. Immune-mediated glomerulopathies (GP) were most common in Southeast Asia, hereditary nephropathies in the Middle East, Africa, and Europe, and systemic GP in Northeast Asia and Latin America. In 14% of cases the cause of KF was unknown, with the highest proportion in Northeast Asia. Disease patterns were also influenced by countries' GNI and average yearly temperature; immune-mediated GP accounted for 43% of diagnoses in low-income countries and were more frequent in warmer climates. Among younger children, congenital anomalies of the kidney and urinary tract (CAKUT) and hereditary nephropathies were the predominant cause of KF, whereas adolescents more commonly presented with immune-mediated GP. CONCLUSION: There is significant global variability in the spectrum of diseases leading to pediatric KF, partially attributable to genetic, environmental, and macroeconomic factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".