Efficacy and Safety of Everolimus Plus Low-Dose Tacrolimus Versus Mycophenolate Mofetil Plus Standard-Dose Tacrolimus in De Novo Renal Transplant Recipients: 12-Month Data
Bibliographic record
Abstract
In this 12-month, multicenter, randomized, open-label, noninferiority study, de novo renal transplant recipients (RTxRs) were randomized (1:1) to receive everolimus plus low-dose tacrolimus (EVR+LTac) or mycophenolate mofetil plus standard-dose Tac (MMF+STac) with induction therapy (basiliximab or rabbit anti-thymocyte globulin). Noninferiority of composite efficacy failure rate (treated biopsy-proven acute rejection [tBPAR]/graft loss/death/loss to follow-up) in EVR+LTac versus MMF+STac was missed by 1.4%, considering the noninferiority margin of 10% (24.6% vs. 20.4%; 4.2% [−3.0, 11.4]). Incidence of tBPAR (19.1% vs. 11.2%; p < 0.05) was significantly higher, while graft loss (1.3% vs. 3.9%; p < 0.05) and composite of graft loss/death/lost to follow-up (6.1% vs. 10.5%, p=0.05) were significantly lower in EVR+LTac versus MMF+STac groups, respectively. Mean estimated glomerular filtration rate was similar between EVR+LTac and MMF+STac groups (63.1 [22.0] vs. 63.1 [19.5] mL/min/1.73 m 2 ) and safety was comparable. In conclusion, EVR+LTac missed noninferiority versus MMF+STac based on the 10% noninferiority margin. Further studies evaluating optimal immunosuppression for improved efficacy will guide appropriate dosing and target levels of EVR and LTac in RTxRs.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".