Cyclosporine Sparing with Mycophenolate Mofetil, Daclizumab and Corticosteroids in Renal Allograft Recipients: The CAESAR Study
Bibliographic record
Abstract
Although the calcineurin inhibitors (CNI) cyclosporine (CsA) and tacrolimus are highly effective immunosuppressants, they are associated with serious side effects. There is great interest in immunosuppressive regimens that permit reduction or elimination of CNIs, while maintaining adequate immunosuppression and acceptable acute rejection rates. Patients (n = 536) receiving their first renal allograft were randomized to one of three immunosuppressant regimens: daclizumab, mycophenolate mofetil (MMF), corticosteroids (CS) and low-dose CsA (target trough levels of 50-100 ng/mL), weaned from month 4 and withdrawn by month 6; daclizumab, MMF, CS and low-dose CsA; or MMF, CS and standard-dose CsA. Mean GFR 12 months after transplantation (primary end point) was not statistically different in the CsA withdrawal and low-dose CsA groups (both 50.9 mL/min/1.73 m(2)) vs. the standard-dose CsA group (48.6 mL/min/1.73 m(2)). At 12 months, the incidence of biopsy-proven acute rejection was significantly higher in the CsA withdrawal group (38%) vs. the low- or standard-dose CsA groups (25.4% and 27.5%, respectively; p < 0.05). In summary, a regimen of continuous low-dose CsA with MMF, CS and daclizumab induction is a clinically safe and effective immunosuppressive regimen in renal transplant recipients.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".