The association of pretransplant dialysis exposure with transplant failure is dependent on the state-specific rate of dialysis mortality
Bibliographic record
Abstract
Longer pretransplant dialysis exposure is associated with a higher risk of transplant failure. Whether patients who receive dialysis in a region with a higher rate of dialysis mortality are a higher risk for transplant failure is unknown. Adjusted state-specific hemodialysis mortality rates were determined in 3-year intervals among prevalent dialysis patients in the United States between 1995 and 2012. The effect of state- and period-specific dialysis mortality on the association of pretransplant dialysis exposure with transplant survival through December 2017 was determined using multivariable models. Dialysis mortality within states ranged from 128 deaths/1000 patient-years to 330 deaths/1000 patient-years. Each additional year of dialysis was associated with a 4% higher risk of transplant failure in states within the lowest quartile of dialysis mortality, compared with an 8% higher risk in states within the highest quartile of dialysis mortality. Patients who received pretransplant dialysis treatment in a state with a high rate of dialysis mortality are at a higher risk for transplant failure compared with patients with the same duration of pretransplant dialysis treatment in a state with a lower mortality rate. The findings may have implications for dialysis care in transplant candidates and the design of future outcome metrics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".