New-onset diabetes mellitus after kidney transplantation: Current status and future directions
Bibliographic record
Abstract
A diagnosis of new-onset diabetes after transplantation (NODAT) carries with it a threat to the renal allograft, as well as the same short- and long-term implications of type 2 diabetes seen in the general population. NODAT usually occurs early after transplantation, and is usually diagnosed according to general population guidelines. Non-modifiable risk factors for NODAT include advancing age, African American, Hispanic, or South Asian ethnicity, genetic background, a positive family history for diabetes mellitus, polycystic kidney disease, and previously diagnosed glucose intolerance. Modifiable risk factors for NODAT include obesity and the metabolic syndrome, hepatitis C virus and cytomegalovirus infection, corticosteroids, calcineurin inhibitor drugs (especially tacrolimus), and sirolimus. NODAT affects graft and patient survival, and increases the incidence of post-transplant cardiovascular disease. The incidence and impact of NODAT can be minimized through pre- and post-transplant screening to identify patients at higher risk, including by oral glucose tolerance tests, as well as multi-disciplinary care, lifestyle modification, and the use of modified immunosuppressive regimens coupled with glucose-lowering therapies including oral hypoglycemic agents and insulin. Since NODAT is a major cause of post-transplant morbidity and mortality, measures to reduce its incidence and impact have the potential to greatly improve overall transplant success.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".