International prevalence patterns of low eGFR in adults aged 18-60 without traditional risk factors from a population-based cross-sectional disadvantaged populations eGFR epidemiology (DEGREE) study
Bibliographic record
Abstract
The disadvantaged populations eGFR (estimated glomerular filtration rate) epidemiology (DEGREE) study was designed to gain insight into the burden of chronic kidney disease (CKD) of undetermined cause (CKDu) using standard protocols to estimate the general-population prevalence of low eGFR internationally. Therefore, we estimated the age-standardized prevalence of eGFR under 60 ml/min per 1.73m 2 in adults aged 18-60, excluding participants with commonly known causes of CKD; an ACR (albumin/creatinine ratio) over 300 mg/g or equivalent, or self-reported or measured (HT) hypertension or (DM) diabetes mellitus, stratified by sex and location. We included population-representative surveys conducted around the world that were either designed to estimate CKDu burden or were re-analyses of large surveys. There were 60,964 participants from 43 areas across 14 countries, with data collected 2007- 2023. The highest prevalence was seen in rural men in Uddanam, India (14%) and Northwest Nicaragua (14%). Prevalence above 5% was generally only observed in rural men, with exceptions for rural women in Ecuador (6%) and parts of Uddanam (6%‒8%), and for urban men in Leon, Nicaragua (7%). Outside of Central America and South Asia, prevalence was below 2%. Our observations represent the first attempts to estimate the prevalence of eGFR under 60 without commonly known causes of CKD around the world, as an estimate of CKDu burden, and provide a starting point for global monitoring. It is not yet clear what drives the differences, but available evidence supports a high general-population burden of CKDu in multiple areas within Central America and South Asia, although the possibility that unidentified clusters of disease may exist elsewhere cannot be excluded.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".