The Etiology of Advanced Chronic Kidney Disease in Southeast Asia: A Meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease (CKD) etiology varies greatly between developed and developing countries. In addition, differences in underlying pathogenesis and therapeutic options affect the progression towards advanced-CKD. This meta-analysis aims to identify the etiology of advanced-CKD in Southeast Asia. METHODS: A systematic search in four electronic-databases and complementary search on national kidney registries and repository libraries was conducted until July 20, 2023. The risk of bias was assessed using Newcastle-Ottawa Scale for observational studies and Version-2 of Cochrane for intervention studies. A random-effects model was used to estimate pooled prevalence. The protocol is registered in the International Prospective Register of Systematic Reviews PROSPERO; Registration ID:CRD42022300786. RESULTS: We analyzed 81 studies involving 32,834 subjects. The pooled prevalence of advanced-CKD etiologies are diabetic kidney disease (DKD) 29.2% (95%CI 23.88-34.78), glomerulonephritis 20.0% (95%CI 16.84-23.38), hypertension 16.8% (95%CI 14.05-19.70), other 8.6% (95%CI 6.97-10.47), unknown 7.5% (95%CI 4.32-11.50), and polycystic kidney disease 0.7% (95%CI 0.40-1.16). We found a significant increase in DKD prevalence from 21% (9.2%, 95%CI 0.00-33.01) to 30% (95%CI 24.59-35.97) before and after the year 2000. Among upper-middle-income and high-income countries, DKD is the most prevalent (26.8%, 95%CI 21.42-32.60 and 38.9%, 95%CI 29.33-48.79, respectively), while glomerulonephritis is common in lower-middle-income countries (33.8%, 95%CI 15.62-54.81). CONCLUSION: The leading cause of advanced-CKD in Southeast Asia is DKD, with a substantial proportion of glomerulonephritis. An efficient screening program targeting high-risk populations (diabetes mellitus and glomerulonephritis) is needed, with the aim to delay CKD progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".