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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".