Change in Mortality Risk Over Time in Young Kidney Transplant Recipients
Bibliographic record
Abstract
Mortality risk for kidney transplant recipients may change with increasing accumulated exposure to the "transplantation milieu." We sought to characterize changes over time in mortality rate and in age-, sex- and race-standardized mortality ratios (SMR) relative to the general population, and to estimate the association between increasing time since first transplant and mortality risk. A total of 18 911 patients who received a first transplant at <21 years old (1983-2006), and whose data were recorded in the USRDS, were studied. There were 2713 deaths over a median follow-up of 8.9 (interquartile range 4.0-14.5; maximum 23) years. Among those with graft function, mortality was highest in the first post transplant year; beyond the first year of the first transplant, age-adjusted mortality rates and SMRs decreased slightly over follow-up. Cause of death was cardiovascular for 34.6%, infection for 19.5%, malignancy for 5.8%, other for 21.4% and unknown for 18.7%. For every 1-year time increment after the end of the first post transplant year, age-adjusted all-cause and cardiovascular mortality rates fell by 1% (p = 0.06) and 16% (p = 0.007), respectively; infection-related mortality rate did not change over time (p = 0.5). These results suggest that exposure to the transplantation milieu has no cumulative negative effects on cardiovascular health over the long term.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".