Mortality and cardiovascular events in new onset diabetes mellitus after renal transplantation
Bibliographic record
Abstract
Introduction: By using multiple fasting blood glucose determinations before and after transplantation, we answer the following question: what are the prevalence, risk factors, and impact on prognosis of new onset diabetes in renal transplant patients? Methods: This was a single-centre, retrospective, observational cohort study of 289 adult patients without diabetes who underwent renal transplantation. Results: New onset diabetes developed in 87 patients (30%) 1.83 months after discharge. Patients with new onset diabetes were treated with long-acting insulin (n = 63, 68%), metformin (n = 77, 80%), and glibenclamide (n = 62, 65%); more than 70% received statins and aspirin. Multivariate analysis (Cox regression analysis) showed that age (HR 1.034, 95% CI 1.013–1.055), body mass index (HR = 1.091, 95% CI 1.035–1.150), and transplant era (HR = 0.148, 95% CI 0.078–0.284) were the independent predictors of new onset diabetes. No difference was found in the incidence of cardiovascular events (log-rank = 0.207) and death (log-rank = 0.319). Discussion: In patients free of diabetes before transplantation, de novo diabetes mellitus is common and related to discrete alterations in metabolic profile before transplantation. A careful evaluation of the metabolic profile of patients on the waiting list would identify patients prone to develop new onset diabetes. New onset diabetes was not related to the incidence of events or death.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".