Mortality and Morbidity in Kidney Transplant Recipients With a Failing Graft: A Matched Cohort Study
Bibliographic record
Abstract
Background: Due to their history of renal disease and exposure to immunosuppression, kidney transplant recipients with a failing graft may be at higher risk of adverse outcomes compared to nontransplant controls. Understanding the burden of disease in transplant recipients may inform treatment decisions of people whose native kidneys are failing and may be eligible for a transplant. Objective: To compare mortality and morbidity in kidney transplant recipients with a failing graft to matched nontransplant controls. Design: Retrospective cohort study. Setting: Alberta, Canada. Patients: Kidney transplant recipients with a failing graft were identified as having at least 2 estimated glomerular filtration rate (eGFR) measurements between 15-30 mL/min/1.73 m 2 (90-365 days apart). We also identified nontransplant controls with a similar degree of kidney dysfunction. Measurements: Mortality and hospitalization. Methods: We propensity-score matched 520 kidney transplant recipients with a failing graft to 520 nontransplant controls. Results: The median age of the matched cohort was 57 years and 40% were women. Compared to matched nontransplant controls, recipients with a failing graft had a higher hazard of death (hazard ratio, 1.54; 95% confidence interval [CI], 1.28-1.85; p < .001) and a higher rate of all-cause hospitalization (rate ratio, 1.67; 95% CI, 1.42-1.97; p < .001). Kidney transplant recipients also had a higher rate of several cause-specific hospitalizations including genitourinary, cardiovascular, and infectious causes. Limitations: Observational design with the risk of residual confounding. Conclusions: A failing kidney transplant is associated with an increased burden of mortality and morbidity beyond chronic kidney disease. This information may assist the discussion of prognosis in kidney transplant recipients with a failing graft and the design of strategies to minimize 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".