Progression of Kidney Disease in Kidney Transplant Recipients With a Failing Graft: A Matched Cohort Study
Bibliographic record
Abstract
Background: Renal function may decline more rapidly in kidney transplant recipients with a failing graft than in people with chronic kidney disease (CKD) of their native kidneys. Methods: We conducted a retrospective, population-based cohort study using linked healthcare databases in Alberta, Canada (2002-2019) to identify kidney transplant recipients with a failing graft, defined as 2 outpatient estimated glomerular filtration rate (eGFR) measurements between 15 and 30 mL/min/1.73 m2 at least 90 days apart. Recipients were compared to propensity-score matched, non-transplant controls with a similar degree of sustained kidney dysfunction who were followed by a nephrologist. We compared the change in eGFR over time (primary outcome) and the competing risks of kidney failure and death without kidney failure (secondary outcome). We used joint modelling to account for possible informative censoring and the association between time-dependent changes in eGFR (eGFR with 95% confidence limits, LCLeGFRUCL) and the competing events (hazard ratios, LCLHRUCL). Results: We matched 575 transplant recipients to 575 non-transplant controls. For the recipients, the median age was 57 years (interquartile range [IQR] 46-67), 39% were women, and median potential follow-up time was 7.8 years (IQR 3.6-12.1). In the joint model, the eGFR decline over time was similar in the two groups (recipients vs. controls: -2.60-2.27-1.94 vs. -2.52-2.21-1.90 mL/min/1.73 m2 per year). In the time-to-event submodel, the hazards for both kidney failure (HR 2.052.683.49) and death (HR 1.231.612.11) were significantly higher for transplant recipients. eGFR decline was associated with kidney failure but not with death. Conclusions: Although kidney function declines at a similar rate in transplant recipients as in non-transplant controls, people with a failing graft have a higher risk of kidney failure and death. Studies are needed to identify preventive measures to improve outcomes in kidney transplant recipients with a failing graft. Funding: Government Support - Non-U.S.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".