Genetic and environmental effects and characteristics of Japanese end‐stage renal disease patients
Bibliographic record
Abstract
Few studies of end-stage renal disease (ESRD) investigate genetic and environmental effects simultaneously in one racial/ethnic group. United States Renal Data System data show racial differences in primary causes of ESRD, survival rates, and causes of death. Comparing these with Japanese Society for Dialysis Therapy data, survival rates appear better for Japanese than for US patients. To explore genetic and environmental differences, we investigated incident and prevalent ESRD patient characteristics. The United States Renal Data System and Japanese Society for Dialysis Therapy databases were analyzed between 1983 and 2002 for the following patient subsets: Americans excluding Asian Americans (n=1,153,974); Asian Americans excluding Japanese Americans (n=35,983); Hawaiian and non-Hawaiian Japanese Americans by state, race, and Japanese surname (n=3932); native Japanese living in Japan (n=450,593). Japanese Americans tended to be older, male, have more diabetes and hypertension and less glomerulonephritis, and to die more often of heart failure than the other US groups. Adjusted mortality hazard ratios were 0.70 for non-Japanese Asian Americans and 0.75 for Japanese Americans vs. non-Asian Americans (1.00). Hawaiian Japanese patients tended to be older, with more diabetes and hypertension and less glomerulonephritis than the other Japanese groups; their survival rates improved after adjustment for rate of diabetes. Japanese American ESRD patients differ from Asian and non-Asian Americans, and from native Japanese, despite similar genetic make-ups. Both genetic and environmental factors may affect 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 | 0.000 | 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 | 0.000 | 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".