Differences in Dialysis Practice Are the Main Reasons for the High Mortality Rate in the United States Compared to Japan
Bibliographic record
Abstract
The cumulative survival of Japanese hemodialysis patients is more than 2.5 times better than that of dialysis patients in the United States (U.S.). The difference is particularly pronounced in older patients, being 4 times better in patients over the age of 50 years. The mortality in U.S. patients has increased from 10 to 25% over the last three decades, but has remained stable at around 10% in Japan. There is no obvious difference in patient selection. The Japanese accept almost as high a proportion of diabetic patients as does the United States, and the mean age of incident patients is higher in Japan. Renal transplantation, virtually absent in Japan, should increase mortality in U.S. dialysis patients by removing patients with the highest probability of survival, but even if one adds surviving transplant patients and studies prevalent populations, the survival rate is much better in Japan. Genetic factors are unlikely to explain differences in mortality, as older Americans live much longer than older Japanese. We speculate that the difference lies in the practice of dialysis. Patients in the United States are generally treated by much faster and shorter dialysis than in Japan. This puts a severe burden on the cardiovascular system of older patients, leading to the poorer survival rate. Japanese physicians also appear to be better trained in dialysis and to spend more time with their patients. The nursing shortage in the United States may also contribute to the increased mortality. Whatever the explanations, the U.S. dialysis community must work to equal and, hopefully, surpass the now superior survival of Japanese dialysis patients.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".