Impact of graft failure on patient survival on dialysis: a comparison of transplant-naive and post-graft failure mortality rates
Bibliographic record
Abstract
BACKGROUND: While the number of patients returning to dialysis after graft failure (GF) is increasing steadily, the impact of a failed kidney transplant on mortality among dialysis patients has not been studied well. METHODS: Data from the Canadian Organ Replacement Register were utilized to examine the outcomes of an incident cohort of patients (n = 25,632) initiating renal replacement therapy (RRT) between 1990 and 1998. Cox regression was used to compare covariate-adjusted mortality among five RRT categories: transplant-naive dialysis, cadaveric primary renal transplant, living-donor primary renal transplant, post-GF dialysis and retransplant. RRT category-specific hazard ratios (HR) were estimated using Cox regression and adjusting for age, sex, race, calendar period, primary renal diagnosis and comorbid conditions. RESULTS: Mortality among post-GF dialysis patients was approximately equal to that of transplant-naive patients (HR = 0.90; P = 0.30) while the HR for retransplanted patients was significantly decreased, relative to the transplant-naive group (HR = 0.35; P<0.01). Diabetes was found to be a significantly (P<0.01) stronger mortality risk factor among post-GF dialysis patients (HR = 3.71) compared with the transplant-naive group (HR = 1.73). In the post-GF group, cardiovascular disease (HR = 1.66) and 'other serious illness' (HR = 2.07) were found to be much stronger risk factors for mortality than in the transplant-naive group (HR = 1.33 and 1.43, respectively), although the differences failed to reach statistical significance. CONCLUSIONS: These results suggest that transplant-naive and post-GF dialysis patients have equivalent mortality risk and that mortality is significantly reduced upon retransplantation. In addition, the results highlight the importance of diabetes and, possibly, comorbid conditions as potential modifiable risk factors in the management of post-GF dialysis patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".