Progression of Kidney Disease in Kidney Transplant Recipients With a Failing Graft: A Matched Cohort Study
Bibliographic record
Abstract
Background: Few studies have assessed outcomes in transplant recipients with failing grafts as most studies have focused on outcomes after graft loss. Objective: To determine whether renal function declines faster in kidney transplant recipients with a failing graft than in people with chronic kidney disease of their native kidneys. Design: Retrospective cohort study. Setting: Alberta, Canada (2002-2019). Patients: We identified kidney transplant recipients with a failing graft (2 estimated glomerular filtration rate [eGFR] measurements 15-30 mL/min/1.73 m 2 ≥90 days apart). Measurements: We compared the change in eGFR over time (eGFR with 95% confidence limits, LCL eGFR UCL ) and the competing risks of kidney failure and death (cause-specific hazard ratios [HRs], LCL HR UCL ). Methods: Recipients (n = 575) were compared with propensity-score-matched, nontransplant controls (n = 575) with a similar degree of kidney dysfunction. Results: The median potential follow-up time was 7.8 years (interquartile range, 3.6-12.1). The hazards for kidney failure (HR 1.10 1.33 1.60 ) and death (HR 1.21 1.59 2.07 ) were significantly higher for recipients, while the eGFR decline over time was similar (recipients vs controls: –2.60 –2.27 –1.94 vs –2.52 –2.21 –1.90 mL/min/1.73 m 2 per year). The rate of eGFR decline was associated with kidney failure but not death. Limitations: This was a retrospective, observational study, and there is a risk of bias due to residual confounding. Conclusions: Although eGFR declines at a similar rate in transplant recipients as in nontransplant controls, recipients have a higher risk of kidney failure and death. Studies are needed to identify preventive measures to improve outcomes in transplant recipients with a failing graft.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".