Association Between Age and Graft Failure Rates in Young Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Age at transplant and graft failure risk are associated in young kidney transplant recipients. The risk of graft failure may also vary by current age, irrespective of age at transplant. We sought to estimate age-specific graft failure rates in young kidney transplant recipients and to estimate the relative hazards of graft failure at different ages, compared with at the age of 25 to 29 years. METHODS: We evaluated 90,689 patients recorded in the United States Renal Data System database who received a first transplant when younger than 40 years (1988-2009); 18,310 were younger than 21 years at transplant. Time-dependent Cox models with time-varying covariates were used to estimate the association between age (time-dependent) and death-censored graft failure risk, adjusted for time since transplant and other potential confounders. RESULTS: There were 31,857 graft failures over a median follow-up of 5.9 years (interquartile range, 2.5-10.5 years; maximum, 21.8 years). Crude age-specific graft failure rates were highest in 19 year olds (6.6 per 100 person-years). Compared with individuals with the same time since transplant observed at 25 to 29 years of age, death-censored graft failure rates were highest in 17 to 24 year olds (hazard ratio, 1.20; [95% confidence interval 1.13, 1.27] for 17-20 year olds and 1.20 [1.13, 1.26] for 21-24 year olds; both P<0.0001) and lowest in 5 to 12 year olds (hazard ratio, 0.60; [0.53, 0.68] for 5-9 year olds and 0.56 [0.49, 0.64] for 10-12 year olds; both P<0.0001). CONCLUSION: Among first kidney transplant recipients younger than 40 years, older adolescents and young adults (17-24 years) have the highest risk of graft failure, irrespective of age at transplant.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".