Association between diabetic status and risk of all-cause and cause-specific mortality on dialysis following first kidney allograft loss
Bibliographic record
Abstract
Background: Diabetes mellitus (DM) is associated with a greater risk of mortality in kidney transplant patients, primarily driven by a greater risk of cardiovascular disease (CVD)-related mortality. However, the associations between diabetes status at time of first allograft loss and mortality on dialysis remain unknown. Methods: All patients with failed first kidney allografts transplanted in Australia and New Zealand between 2000 and 2020 were included. The associations between diabetes status at first allograft loss, all-cause and cause-specific mortality were examined using competing risk analyses, separating patients with diabetes into those with pre-transplant DM or post-transplant diabetes mellitus (PTDM). Results: Of 3782 patients with a median (IQR) follow-up duration of 2.7 (1.1-5.4) years, 539 (14%) and 390 (10%) patients had pre-transplant DM or developed PTDM, respectively. In the follow-up period, 1336 (35%) patients died, with 424 (32%), 264 (20%) and 199 (15%) deaths attributed to CVD, dialysis withdrawal and infection, respectively. Compared to patients without DM, the adjusted subdistribution HRs (95% CI) for pre-transplant DM and PTDM for all-cause mortality on dialysis were 1.47 (1.17-1.84) and 1.47 (1.23-1.76), respectively; for CVD-related mortality were 0.81 (0.51-1.29) and 1.02 (0.70-1.47), respectively; for infection-related mortality were 1.84 (1.02-3.35) and 2.70 (1.73-4.20), respectively; and for dialysis withdrawal-related mortality were 1.71 (1.05-2.77) and 1.51 (1.02-2.22), respectively. Conclusions: Patients with diabetes at the time of kidney allograft loss have a significant survival disadvantage, with the excess mortality risk attributed to infection and dialysis withdrawal.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".