Comparative Effectiveness of Tacrolimus-Based Steroid Sparing versus Steroid Maintenance Regimens in Kidney Transplantation: Results from Discrete Event Simulation
Bibliographic record
Abstract
BACKGROUND: Corticosteroids used as immunosuppressants to prevent acute rejection (AR) and graft loss (GL) following kidney transplantation are associated with serious cardiovascular and other adverse events. Evidence from short-term randomized controlled trials suggests that many patients on a tacrolimus-based immunosuppressant regimen can withdraw from steroids without increased AR or GL risk. OBJECTIVES: To measure the long-term tradeoff between GL and adverse events for a heterogeneous-risk population and determine the optimal timing of steroid withdrawal. METHODS: A discrete event simulation was developed including, as events, AR, GL, myocardial infarction (MI), stroke, cytomegalovirus, and new onset diabetes mellitus (NODM), among others. Data from the United States Renal Data System were used to estimate event-specific parametric regressions, which accounted for steroid-sparing regimen (avoidance, early 7-d withdrawal, 6-mo withdrawal, 12-mo withdrawal, and maintenance) as well as patients' demographics, immunologic risks, and comorbidities. Regression-equation results were used to derive individual time-to-event Weibull distributions, used, in turn, to simulate the course of patients over 20 y. RESULTS: Patients on steroid avoidance or an early-withdrawal regimen were more likely to experience AR (45.9% to 55.0% v. 33.6%, P < 0.05) and GL (51.5% to 68.8% v. 37.8%, P < 0.05) compared to patients on steroid maintenance. Patients in 6-mo and 12-mo steroid withdrawal groups were less likely to experience MI (11.1% v. 13.3%, P < 0.05), NODM (30.7% to 34.4% v. 37.7%, P < 0.05), and cardiac death (29.9% to 30.5% v. 32.4%, P < 0.05), compared to steroid maintenance. CONCLUSIONS: Strategies of 6- and 12-mo steroid withdrawal post-kidney transplantation are expected to reduce the rates of adverse cardiovascular events and other outcomes with no worsening of AR or GL rates compared with steroid maintenance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".