A PILOT STUDY OF STEROID-FREE IMMUNOSUPPRESSION IN THE PREVENTION OF ACUTE REJECTION IN RENAL ALLOGRAFT RECIPIENTS
Bibliographic record
Abstract
BACKGROUND: Corticosteroids have been a mainstay of rejection prophylaxis for several decades, despite the multiple adverse effects of long-term use, including weight gain, hyperlipidemia, diabetes, hypertension, and bone disease. The detrimental effect of steroids on the metabolic profile begins in the early posttransplantation period, and the complete avoidance of steroids in transplantation would therefore be optimal. We hypothesized that the addition of mycophenolate mofetil (MMF) and a humanized monoclonal anti-CD25 antibody (daclizumab) to a cyclosporine (CsA microemulsion)-based immunosuppression protocol would permit transplantation without steroids. METHODS: Steroid-free renal transplantation was attempted in 57 patients treated with daclizumab, MMF, and CsA. Twenty-eight patients received kidneys from living donors; the remaining 29 received cadaveric grafts. RESULTS: At 1 year, patient and graft survival were 95% and 89%, respectively. Fourteen patients (25%) experienced rejections, of which 13 were readily reversed with steroids; 1 patient required OKT3. Mean serum creatinine at 12 months for patients not experiencing rejection was 149+/-58 micromol/L, compared with 158+/-102 micromol/L for those experiencing rejection. Five patients required hospitalization for infection; no patients developed lymphoproliferative disease. At baseline, 17 patients required 3 or more antihypertensive medications, compared with 2 patients at 1 year. Three of 43 nondiabetic patients developed diabetes during the study. There was no significant reduction in lumbar or femoral bone density. CONCLUSIONS: On the basis of these positive results, we believe steroid avoidance with this immunosuppressive regimen merits further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".