Evaluating the Survival Benefit of Kidney Retransplantation
Bibliographic record
Abstract
BACKGROUND: The magnitude of the survival benefit associated with kidney retransplantation has not been well studied. METHODS: Using data from the Canadian Organ Replacement Register (CORR), we studied patients (n=3,067) initiating renal replacement therapy during 1981-1998 who had received a transplant and experienced graft failure (GF). Such patients were followed until death, loss to follow-up or the end of the observation period (December 31, 1998). Using Cox regression, we estimated the post-GF covariate-adjusted hazard ratio (HR) for retransplant versus dialysis, and determined whether the contrast differed across patient subgroups. Through nonproportional hazards models, we also examine patterns in the retransplant/dialysis HR with time following retransplant. RESULTS: Overall, retransplantation is associated with a covariate-adjusted 50% reduction in mortality, relative to remaining on dialysis (HR=0.50; P<0.0001). This benefit is most pronounced in the 18- to 59-year age group. Retransplanted patients were at significantly higher risk of death relative to patients on dialysis only during the first month posttransplant (HR=1.66; P=0.047), and experienced significantly reduced mortality thereafter. CONCLUSIONS: Following primary graft failure, retransplantation is associated with significantly reduced mortality rates among Canadian end-stage renal disease patients. Further study should be undertaken to assess the applicability of our findings to other patient populations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".