International Comparisons of Survival on Dialysis: Are They Reliable?
Bibliographic record
Abstract
Sharp discrepancies between reported survivals on maintenance hemodialysis in the United States compared with Europe and Japan have provoked broad criticism of the American system of treating irreversible uremia. Although this negative view of renal therapy in the United States is supported by the National Kidney Foundation (NKF), consensus conferences of the National Institutes of Health (NIH), and numerous social critics of the American health care system, the contention has not been sustained by appropriate statistical analysis. The United States has the world's highest treatment rate for incident kidney failure - double that in Europe. This is mainly due to universal acceptance for uremia therapy. In comparisons with Japan, studies have not taken into account unique aspects of Japanese health care and genetic differences between sampled cohorts of studied kidney patients. In fact, no properly conducted analysis has found that the quality of uremia therapy in the U.S. has been inferior to that of anywhere else. While the allegation may yet be proven true, thus far there is no scientific basis for indicting dialysis in the United States as lacking in quality or quantity when ranked with other industrialized nations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.132 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".