The higher risk of death on peritoneal dialysis in the United States is not explained by background general population mortality: the CANUSA study revisited
Bibliographic record
Abstract
The CANUSA investigators reported a near doubling of the risk of death in peritoneal dialysis patients treated at U.S. sites compared to Canadian centers. Recently, evidence has suggested that background mortality rates in the general population might be responsible for differences in death rates on dialysis. The objective of this study was to determine if differences in background mortality in the general population were responsible for the increased risk of death observed in American patients in the CANUSA study. The CANUSA study was a prospective cohort study of 680 consecutive peritoneal dialysis patients at 14 centers in the U.S. and Canada. Extensive baseline data were available for all patients. The expected mortality rate of an individual of the same age, sex, and country of residence was determined at the time of enrollment in the CANUSA study. Cox proportional hazards models were used to determine if background mortality rates were responsible for the observed differences in survival between the two countries. Background mortality rate in the general population was associated with an increased risk of death on peritoneal dialysis, but after adjustment for other baseline factors, it was no longer significant. The adjusted, relative hazard of dying in the U.S. compared to Canada was unchanged after further adjusting for background mortality rate in statistical models (HR = 1.93; 95% confidence interval: 1.13 - 3.28). In conclusion, the increased risk of mortality in U.S. patients enrolled in the CANUSA study was not explained by differences in the background mortality rate in the general population.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".