Induction immunosuppression and post-transplant diabetes mellitus: a propensity-matched cohort study
Bibliographic record
Abstract
Introduction: Post-transplant diabetes mellitus (PTDM) is a common complication among cardiac transplant recipients, causing diabetes-related complications and death. While certain maintenance immunosuppressive drugs increase PTDM risk, it is unclear whether induction immunosuppression can do the same. Therefore, we evaluated whether induction immunosuppression with IL-2 receptor antagonists, polyclonal anti-lymphocyte antibodies, or Alemtuzumab given in the peri-transplant period is associated with PTDM. Methods: We used the Scientific Registry of Transplant Recipients database to conduct a cohort study of US adults who received cardiac transplants between January 2008-December 2018. We excluded patients with prior or multiple organ transplants and those with a history of diabetes, resulting in 17,142 recipients. We created propensity-matched cohorts (n=7,412) using predictors of induction immunosuppression and examined the association between post-transplant diabetes and induction immunosuppression by estimating hazard ratios using Cox proportional-hazards models. Results: In the propensity-matched cohort, the average age was 52.5 (SD=13.2) years, 28.7% were female and 3,706 received induction immunosuppression. There were 867 incident cases of PTDM during 26,710 person-years of follow-up (32.5 cases/1,000 person-years). There was no association between induction immunosuppression and post-transplant diabetes (Hazard Ratio= 1.04, 95% confidence interval 0.91 - 1.19). Similarly, no associations were observed for each class of induction immunosuppression agents and post-transplant diabetes. Conclusion: The use of contemporary induction immunosuppression in cardiac transplant patients was not associated with post-transplant diabetes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".