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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".