Patient Survival Following Renal Transplant Failure in Canada
Bibliographic record
Abstract
Studies from the United States have shown that renal allograft failure is associated with a high mortality rate. The purpose of this study was to determine whether transplant failure was associated with survival in a recent cohort of kidney transplant recipients with different characteristics and a distinct health care system from the United States. Cox regression was used to model allograft loss as a time-dependent variable with patient survival as the primary outcome in 4743 kidney transplant recipients from the Canadian Organ Replacement Register. During follow-up 607 (12.8%) patients had allograft failure and 411 (8.7%) died. Patients with a functioning transplant had an unadjusted death rate of 2.06 per 100 patient years that increased to 5.14 per 100 patient years following allograft failure. After controlling for important confounding variables, allograft failure was found to increase the risk of death by over threefold compared to patients who maintained transplant function (adjusted hazard ratio, 3.39; 95% CI, 2.75-4.16; p < 0.0001). In conclusion, this analysis has shown that kidney transplant failure is an independent predictor of mortality following renal transplantation in a Canadian population. This finding supports the premise that it is the loss of transplant function, rather than patient or system-related issues, that is the main factor contributing to outcome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".