Early Identification of CKD—A Scoping Review of the Global Populations
Bibliographic record
Abstract
Introduction: Decisions on whether to screen for chronic kidney disease (CKD) or not remain contentious in nephrology. This study provides a global overview of early CKD identification efforts. Methods: Guidelines for scoping reviews were followed and studies were identified by searching MEDLINE, EMBASE, Cochrane Library, CINAHL, ISI Web of Science, and PsycINFO. Data extracted from included studies focused on the following 4 themes: study population, measurement methods, interventions used, and available policies. Results: We identified 290 CKD screening and detection programs from 83 countries. Overall sample size was 3.72 million (North East Asia: 1.19 million), detection of CKD was the aim in 97.6%, 63.1% used population-based screening methods, and only 12.4% were in rural populations. Reported CKD prevalence (stages 3-5) was higher in targeted- (14.8%) than population-based studies (8.0%). Number of persons needed to screen (NNS) to identify 1 case was also lower in targeted studies (7 vs. 13). Single measurements (80%) and the combination of estimation of glomerular filtration rate with a urine test (albuminuria/proteinuria) (71.4%) were frequently used to detect CKD. Only 2.8% of studies included an intervention such as pharmacotherapy in identified cases. Policies on early identification were available in 30.1% of countries included. Conclusion: Methods for early CKD identification vary worldwide, often leading to wide variations in the reported prevalence. Efforts to standardize measurement methods for early detection focusing on high-risk populations and ensuring appropriate interventions are available to those identified with CKD will improve the value of programs and improve patient outcomes.
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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 | 0.039 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".