Effectiveness of Early Detection Programs for Chronic Kidney Disease in Children: A Systematic Review
Bibliographic record
Abstract
Background:Chronic Kidney Disease (CKD) in children poses a significant global health challenge, often progressing silently until advanced stages. Early detection is critical to initiate timely interventions that can prevent irreversible renal damage, reduce morbidity, and improve long-term outcomes. Despite advances in diagnostics, the effectiveness and implementation of pediatric CKD early detection programs vary globally. Objective:This systematic review aimed to evaluate the effectiveness of early detection programs for CKD in children, focusing on their impact on early diagnosis, disease progression, and clinical outcomes across different healthcare settings. Methods:Following PRISMA 2020 guidelines, a comprehensive search was conducted in PubMed, Scopus, Web of Science, Embase, and Cochrane Library from January 2015 to September 2025. Eligible studies included randomized controlled trials, cohort, cross-sectional, and case-control studies evaluating early detection or screening programs for pediatric CKD. Data were extracted on study design, screening type, outcomes, and implementation characteristics. Quality assessment was performed using Cochrane RoB 2.0, Newcastle–Ottawa Scale, and JBI tools. Results:Out of 2,161 identified records, 28 studies met inclusion criteria. Most studies originated from Asia (Japan, Korea, India, Iran) and Europe. School-based urinary screening and risk-targeted screening programs demonstrated effectiveness in detecting asymptomatic CKD at earlier stages and facilitating timely nephrology referrals. Biomarker-based approaches—such as cystatin C, netrin-1, NGAL, and KIM-1—significantly improved diagnostic accuracy compared to serum creatinine alone. Integrating biomarker screening with school and primary-care programs improved early detection rates and reduced CKD progression risk. However, cost-effectiveness and sustainability remain major challenges, especially in low- and middle-income countries. Conclusion:Early detection programs for pediatric CKD are effective in identifying renal impairment at subclinical stages, enabling early intervention and slowing disease progression. The combination of population-based screening and biomarker-guided testing offers the best balance between sensitivity and practicality. Future research should prioritize standardizing screening protocols, integrating digital tools and AI-based risk prediction, and evaluating long-term cost-effectiveness to inform global pediatric nephrology policies.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".