The ORION Study: Comparison of Two Sirolimus-Based Regimens versus Tacrolimus and Mycophenolate Mofetil in Renal Allograft Recipients
Bibliographic record
Abstract
Safety and efficacy of two sirolimus (SRL)-based regimens were compared with tacrolimus (TAC) and mycophenolate mofetil (MMF). Renal transplantation recipients were randomized to Group 1 (SRL+TAC; week 13 TAC elimination [n = 152]), Group 2 (SRL + MMF [n = 152]) or Group 3 (TAC + MMF [n = 139]). Group 2, with higher-than-expected biopsy-confirmed acute rejections (BCARs), was sponsor-terminated; therefore, Group 2 two-year data were limited. At 1 and 2 years, respectively, graft (Group 1: 92.8%, 88.5%; Group 2: 90.6%, 89.9%; Group 3: 96.2%, 95.4%) and patient (Group 1: 97.3%, 94.4%; Group 2: 95.2%, 94.5%; Group 3: 97.0%, 97.0%) survival rates were similar. One- and 2-year BCAR incidence was: Group 1, 15.2%, 17.4%; Group 2, 31.3%, 32.8%; Group 3, 8.2%, 12.3% (Group 2 vs. 3, p < 0.001). Mean 1- and 2-year modified intent-to-treat glomerular filtration rates (mL/min) were similar. Primary reason for discontinuation was adverse events (Group 1, 34.2%; Group 2, 33.6%; Group 3, 22.3%; p < 0.05). In Groups 1 and 2, delayed wound healing and hyperlipidemia were more frequent. One-year post hoc analysis of new-onset diabetes posttransplantation was greater in TAC recipients (Groups 1 and 3 vs. 2, 17% vs. 6%; p = 0.004). Between-group malignancy rates were similar. The SRL-based regimens were not associated with improved outcomes for kidney transplantation patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".