Sex differences in chronic kidney disease prevalence in Asia: a systematic review and meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
Background: Previous reports on the prevalence of chronic kidney disease (CKD) in Asia have suggested important sex disparities but have been inconsistent in nature. We sought to synthesize available sex-disaggregated CKD prevalence data in Asia to quantify sex disparities in the region. Methods: We systematically searched MEDLINE and Embase for observational studies involving ≥500 adults who reported sex-disaggregated CKD prevalence data in any of the 26 countries in East, Southeast and South Asia. For each study we calculated the female:male prevalence ratio (PR), with a ratio >1 indicating a higher female prevalence. For each country, log-transformed PRs were pooled using random effects meta-analysis. These were then combined using a fixed effects model, weighting by population size, to estimate a pooled PR for each of East, Southeast and South Asia and Asia overall. Results: Sex-disaggregated data were available from 171 cohorts, spanning 15 countries and comprising 2 550 169 females and 2 595 299 males. Most studies (75.4%) came from East Asia (China, Taiwan, Japan and South Korea). Across Asia, CKD prevalence was higher in females {pooled prevalence 13.0% [95% confidence interval (CI) 11.3-14.9]} compared with males [pooled prevalence 12.1% (95% CI 10.3-14.1)], with a pooled PR of 1.07 (95% CI 0.99-1.17). Substantial heterogeneity was observed between countries. The pooled PRs for East, Southeast and South Asia were 1.11 (95% CI 1.02-1.21), 1.09 (0.88-1.36) and 1.03 (0.87-1.22), respectively. Conclusions: Current evidence suggests considerable between-country and -region heterogeneity in the female:male PR of CKD. However, there remains a large part of the region where data on sex-specific CKD prevalence are absent or limited. Country-level assessment of the differential burden of CKD in females and males is needed to define locally relevant policies that address the needs of both sexes.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it