Reduced Functional Status among Kidney Transplant Recipients over Time and Associated Risks Post‐Transplant
Bibliographic record
Abstract
BACKGROUND: Reduced functional status (RFS) has been associated with death and graft loss after kidney transplant; whether the risk of short-term outcomes, including delayed graft function (DGF) and early graft loss (EGL) is also increased is unknown. The purpose of this study was to examine whether RFS is associated with DGF, EGL, and all-cause graft loss (ACGL) in kidney transplant recipients (KTRs; overall and by recipient age, sex, race, donor status, and transplant era). METHODS: Separately among adult living and deceased donor KTR in the United States identified using the SRTR (2000-2017), multivariable logistic regression and Cox proportional hazards models were used as appropriate to examine the association of RFS (Karnofsky Performance Status [KPS] ≤50%; requires considerable or greater assistance) relative to functional independence (KPS 80%-100%) at the time of kidney transplant with the outcomes of DGF, EGL, and ACGL. Whether recipient age, sex, race, donor status, or transplant era modified the risk of RFS associated with each outcome was also examined. RESULTS: Among 245 446 KTR, RFS at transplant was associated with increased risk of DGF (aOR 1.47, 95% CI 1.36-1.59), EGL (aOR 2.85, 95% CI 2.52-3.24), and ACGL (aHR 1.21, 95% CI 1.14-1.28) in deceased donor KTR, and increased risk of DGF (aOR 1.32, 95% CI 1.01-1.73), EGL (aOR 2.07, 95% CI 1.48-2.89), and ACGL (aHR 1.24, 95% CI 1.11-1.38) in living donor KTR compared to those with functional independence. In deceased donor KTR, the risk of DGF was modified by recipient age, sex, and race, and the risk of EGL was modified by sex. In living donor KTR, the risk of DGF was modified by age, and the risk of ACGL was modified by sex. CONCLUSIONS: RFS at transplant has increased over time and is associated with increased risk of DGF, EGL, and ACGL; recipient characteristics modify these risks.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".