Global prevalence of chronic kidney disease and associated risk factors in children and adolescents: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: While chronic kidney disease (CKD) is well characterised in adults, less is known about the prevalence of CKD in children and adolescents, where it is rare and associated with unique characteristics and implications for long-term health outcomes. This study protocol outlines a systematic review to assess the global prevalence of CKD in children and adolescents along with causes and associated risk factors. This is warranted to better characterise prevalence and to identify at-risk groups that would benefit from screening efforts. We will explore the risk and burden of CKD and its variations by sociodemographic characteristics (age group, race, sex/gender) and geographical regions (country, International Society of Nephrology region and income groups based on World Bank country classifications). METHODS AND ANALYSIS: We will conduct a systematic review of studies reporting on the prevalence of CKD in children and adolescents following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 (PRISMA-P-2015) and the PRISMA 2020 methodological guidelines (PRISMA 2020). Searches will be undertaken in the following databases with the date range from 2000 to date: Ovid MEDLINE, Ovid Embase, CINAHL, Cochrane Library, ProQuest Dissertations & Theses Citation Index (via Clarivate), Web of Science Core Collection, Google Scholar and grey literature sites (registries, government reports) to identify studies that report on the prevalence of CKD in children and adolescents from ages 0 to 18. The primary outcome will be the global prevalence of CKD in children and adolescents. Secondary outcomes will include the causes of and risk factors for CKD, and examining differences and temporal trends in CKD prevalence across countries, geographical regions, income levels and sociodemographic characteristics. ETHICS AND DISSEMINATION: No direct involvement with patient data will be used in this systematic review, as data will be obtained from previously published reports. Ethical approval is therefore not required. Our findings will be published in an open-access peer-reviewed journal and presented at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42024547467.
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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 | 0.072 | 0.125 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.039 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.005 |
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".