Differences in progression of CKD and mortality amongst Caucasian, Oriental Asian and South Asian CKD patients
Bibliographic record
Abstract
BACKGROUND: Ethnic differences in chronic kidney disease (CKD) progression are not well characterized but are of interest across and within countries. METHODS: We followed up a large CKD cohort of patients of three different ethnic origins [Caucasian, Oriental Asian (OA) and South Asian (SA)] from time of nephrology referral in a universal health care system. Key outcomes were time to death and/or renal replacement therapy (RRT) and rate of decline in estimated GFR (eGFR). The effects of known predictors (blood pressure, proteinuria, age, sex, diabetes, cardiovascular disease and medications) and of other laboratory abnormalities were assessed using multivariate modelling techniques, including both Cox proportional hazards and competing risk approach. RESULTS: The cohort comprised 3444 patients (2626 Caucasians, 397 OA and 421 SA). All-cause mortality rates are higher in Caucasians than SA or OA [hazard ratio (HR) 0.693 and 0.803, P < 0.05]. OA and SA have higher risks of progressing to RRT (HR 1.281 and 1.349, P < 0.05) and lower risks of death before RRT (HR 0.718 and 0.520, P < 0.05) compared to Caucasians after adjustment for usual risk factors. However, when adjusted for additional laboratory abnormalities, differences did not persist for progression, but did for survival advantage of Asians. The median rate of decline in eGFR (in millilitres per minute per 1.73 m(2)) was significantly slower in Caucasians (-2.11) than in OA (-2.93) or SA (-3.56), P = 0.027. CONCLUSIONS: Asians appear to have faster CKD progression and lower mortality rates compared to Caucasians. This effect is not explained by the usual variables, but rates of progression may be related to differences in severity of laboratory abnormalities at different CKD stages. Further research is needed to understand the implications of these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".