Recent advances in new-onset diabetes mellitus after kidney transplantation
Bibliographic record
Abstract
A common challenge in managing kidney transplant recipients (KTR) is post-transplant diabetes mellitus (PTDM) or diabetes mellitus (DM) newly diagnosed after transplantation, in addition to known pre-existing DM. PTDM is an important risk factor for post-transplant cardiovascular (CV) disease, which adversely affects patient survival and quality of life. CV disease in KTR may manifest as ischemic heart disease, heart failure, and/or left ventricular hypertrophy. Available therapies for PTDM include most agents currently used to treat type 2 diabetes. More recently, the use of sodium glucose co-transporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1 RA), and dipeptidyl peptidase 4 inhibitors (DPP4i) has cautiously extended to KTR with PTDM, even though KTR are typically excluded from large general population clinical trials. Initial evidence from observational studies seems to indicate that SGLT2i, GLP-1 RA, and DPP4i may be safe and effective for glycemic control in KTR, but their benefit in reducing CV events in this otherwise high-risk population remains unproven. These newer drugs must still be used with care due to the increased propensity of KTR for intravascular volume depletion and acute kidney injury due to diarrhea and their single-kidney status, pre-existing burden of peripheral vascular disease, urinary tract infections due to immunosuppression and a surgically altered urinary tract, erythrocytosis from calcineurin inhibitors, and reduced kidney function from acute or chronic rejection.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".