Immunosuppression with Belatacept-Based, Corticosteroid-Avoiding Regimens in De Novo Kidney Transplant Recipients
Bibliographic record
Abstract
Current immunosuppressive regimens in renal transplantation typically include calcineurin inhibitors (CNIs) and corticosteroids, both of which have toxicities that can impair recipient and allograft health. This 1-year, randomized, controlled, open-label, exploratory study assessed two belatacept-based regimens compared to a tacrolimus (TAC)-based, steroid-avoiding regimen. Recipients of living and deceased donor renal allografts were randomized 1:1:1 to receive belatacept-mycophenolate mofetil (MMF), belatacept-sirolimus (SRL), or TAC-MMF. All patients received induction with 4 doses of Thymoglobulin (6 mg/kg maximum) and an associated short course of corticosteroids. Eighty-nine patients were randomized and transplanted. Acute rejection occurred in 4, 1 and 1 patient in the belatacept-MMF, belatacept-SRL and TAC-MMF groups, respectively, by Month 6; most acute rejection occurred in the first 3 months. More than two-thirds of patients in the belatacept groups remained on CNI- and steroid-free regimens at 12 months and the calculated glomerular filtration rate was 8-10 mL/min higher with either belatacept regimen than with TAC-MMF. Overall safety was comparable between groups. In conclusion, primary immunosuppression with belatacept may enable the simultaneous avoidance of both CNIs and corticosteroids in recipients of living and deceased standard criteria donor kidneys, with acceptable rates of acute rejection and improved renal function relative to a TAC-based regimen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".