Impact of diabetes mellitus on clinical outcomes after heart transplantation
Bibliographic record
Abstract
PURPOSE: Diabetes mellitus (DM) is common among recipients of heart transplantation (HTx) but its impact on clinical outcomes is unclear. We evaluated the associations between pretransplant DM and posttransplant DM (PTDM) and outcomes among adults receiving HTx at a single center. METHODS: We performed a retrospective study (range 01/2008 - 07/2018), n = 244. The primary outcome was survival; secondary outcomes included acute rejection, cardiac allograft vasculopathy, infection requiring hospitalization, macrovascular events, and dialysis initiation post-transplant. Comparisons were performed using Kaplan-Meier and multivariable Cox regression analyses. RESULTS: Pretransplant DM was present in 75 (30.7%) patients and was associated with a higher risk for infection requiring hospitalization (p < 0.05), but not with survival or other outcomes. Among the 144 patients without pretransplant DM surviving to 1 year, 29 (20.1%) were diagnosed with PTDM at the 1-year follow-up. After multivariable adjustment, PTDM diagnosis at 1-year remained associated with worse subsequent survival (hazard ratio 2.72, 95% confidence interval 1.03-7.16). Predictors of PTDM at 1-year included cytomegalovirus seropositivity and higher prednisone dose (> 5 mg/day) at 1-year follow-up. CONCLUSIONS: Compared to HTx recipients without baseline DM, those with baseline DM have a higher risk for infections requiring hospitalization, and those who develop DM after HTx have worse survival.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".