DACLIZUMAB (HUMANIZED ANTI-IL2R?? MAB) PROPHYLAXIS FOR PREVENTION OF ACUTE REJECTION IN RENAL TRANSPLANT RECIPIENTS WITH DELAYED GRAFT FUNCTION1,2
Bibliographic record
Abstract
BACKGROUND: The purpose of this retrospective study was to determine the benefits of daclizumab, (Zenapax, Roche Pharmaceuticals) a humanized anti-interleukin-2Ralpha (IL-2Ralpha) monoclonal antibody, for prevention of acute rejection in renal transplant recipients with delayed graft function (DGF). METHODS: Data from two multicenter randomized placebo-controlled trials were pooled. DGF was defined by urine output <30 cc/hour, decline in serum creatinine of <0.5 mg/dl, or the need for dialysis within the first 24 hours after transplantation. RESULTS: At one year posttransplantation, the incidence of biopsy-proven acute rejection in patients with DGF was reduced from 44% in the placebo group to 28% in the daclizumab group. (P=0.03) Prophylaxis with daclizumab also delayed the onset of the first biopsy-proven acute rejection episode in patients with DGF from 29+/-43 days in the placebo group to 73+/-70 days in the daclizumab group. (P=0.004) The graft survival rates in patients with DGF at 1 year posttransplantation were 78% in the placebo group and 82% in the daclizumab treated group. (P=ns) Three patients in the placebo-treated group with DGF experienced graft loss due to acute rejection, whereas no patients in the daclizumab-treated group with DGF had graft loss due to acute rejection. The 1-year patient survival rate in those with DGF in the placebo and daclizumab groups were 93% and 98%, respectively. (P=ns) CONCLUSIONS: Daclizumab effectively reduced the incidence and delayed the onset of biopsy-proven acute rejection in this high-risk subgroup of patients with DGF after renal transplantation. Graft and patient survival rates were similar between placebo- and daclizumab-treated patients with DGF.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".