Impact of Pretransplant and New-Onset Diabetes After Transplantation on the Risk of Major Adverse Cardiovascular Events in Kidney Transplant Recipients: A Population-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Pretransplant diabetes and new-onset diabetes after transplant (NODAT) are known risk factors for vascular events after kidney transplantation, but the incidence and magnitude of the risk of major adverse cardiovascular events (MACE) and cardiac deaths remain uncertain in recent era. METHODS: A population cohort study of kidney transplant recipients identified using data from linked administrative healthcare databases from Ontario, Canada. The incidence rates of MACE (expressed as events with 95% confidence interval [95% CI] per 1000 person-years were reported according to diabetes status of pretransplant diabetes, NODAT, or no diabetes. Extended Cox regression model was used to examine the association between diabetes status, MACE, and cardiac death. RESULTS: Of 5248 recipients, 1973 (38%) had pretransplant diabetes, and 799 (15%) developed NODAT with a median follow-up of 5.5 y. The incidence rates (95% CI) of MACE for recipients with pretransplant diabetes, NODAT, and no diabetes between 1 and 3 y posttransplant were 38.1 (32.1-45.3), 12.6 (6.3-25.2), and 11.8 (9.2-15.0) per 1000 person-years, respectively. Compared with recipients with pretransplant diabetes, recipients with NODAT experienced a lower risk of MACE (adjusted hazard ratio, 0.59; 95% CI, 0.47-0.74) but not cardiac death (adjusted hazard ratio, 0.97; 95% CI, 0.61-1.55). The rate of MACE and cardiac death was lowest in patients without diabetes. CONCLUSIONS: Patients with pretransplant diabetes incur the greatest rate of MACE and cardiac deaths after transplantation. Having NODAT also bears high burden of vascular events compared with those without diabetes, but the magnitude of the increased rate remains lower than recipients with pretransplant diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".