Temporal Changes on the Risks and Complications of Posttransplantion Diabetes Mellitus Following Cardiac Transplantation
Bibliographic record
Abstract
Background . Recent changes in the demographic of cardiac donors and recipients have modulated the rate and risk, associated with posttransplant diabetes mellitus (PTDM). We investigated the secular trends of the risk of PTDM at 1 year and 3 years after transplantation over 30 years and explored its effect on major outcomes. Methods . Three hundred and three nondiabetic patients were followed for a minimum of 36 months, after a first cardiac transplantation performed between 1983 and 2011. Based on the year of their transplantation, the patients were divided into 3 eras: (1983-1992 [era 1], 1993-2002 [era 2], and 2003-2011 [era 3]). Results . In eras 1, 2, and 3, the proportions of patients with PTDM at 1 versus 3 years were 23% versus 39%, 21% versus 26%, and 33% versus 38%, respectively. Independent risk factors predicting PTDM at one year were recipient’s age, duration of cold ischemic time, treatment with furosemide, and tacrolimus. There was a trend for overall survival being worse for patients with PTDM in comparison to patients without PTDM ( p = 0.08). Patients with PTDM exhibited a significantly higher rate of renal failure over a median follow-up of 10 years ( p = 0.03). Conclusion . The development of PTDM following cardiac transplantation approaches 40% at 3 years and has not significantly changed over thirty years. The presence of PTDM is weakly associated with an increased mortality and is significantly associated with a worsening in renal function long-term following cardiac transplantation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".