Differences in cardiovascular mortality rates among hemodialysis patients in the United States and Japan: The importance of background cardiovascular mortality
Bibliographic record
Abstract
Mortality rates among hemodialysis patients differ greatly among the United States, Europe, and Japan and it has been hypothesized that this is mainly due to differences in practice patterns. Results from the international DOPPS study, however, indicate that differences in practice patterns among the United States, Japan, and Europe are small and not alone explanatory for the differences in mortality rates. Ethnic variability in predisposition to atherosclerotic cardiovascular disease in the general population may lead to significant differences in background cardiovascular mortality in the United States, Japan, and Europe. It is our hypothesis that cardiovascular mortality in dialysis patients is to a great extent dependent on cardiovascular background mortality of the general population. We are currently studying the relationship between all-cause and cardiovascular death rates in countries worldwide using the WHO database. Preliminary data from 35 countries show that all-cause and cardiovascular death rates differ significantly among regions, with Eastern European countries reporting four- to sevenfold higher death rates than Asian countries. A strong linear relationship between cardiovascular and all-cause death rates is observed among these countries. The next step of our study will be to compare country-specific cardiovascular death rates of dialysis populations with those of the respective general populations. Ethnic differences in cardiovascular morbidity and mortality may be explained by genetic variability based upon polymorphism of genes involved in the pathogenesis of atherosclerosis and myocardial infarction.
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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".