Thymoglobulin Versus Basiliximab Induction Therapy for Simultaneous Kidney-Pancreas Transplantation: Impact on Rejection, Graft Function, and Long-Term Outcome
Bibliographic record
Abstract
BACKGROUND: Thymoglobulin (ATG) and basiliximab induction therapies are used by the majority of centers for pancreas transplantation today. Although both strategies have different mechanisms, there is a paucity of studies comparing them. We compared the efficacy and side effects of both methods in simultaneous pancreas-kidney (SPK) transplantation. METHODS: We analyzed 128 SPKs at our institution between January 2001 and August 2008. Forty-nine patients received basiliximab (40 mg), whereas 79 patients had ATG (5 mg/kg). Graft function, complications, rejection, and survival rates were analyzed. RESULTS: ATG versus basiliximab therapy was associated with decreased 3-month (6% vs. 21%; P=0.01) and 1-year (14% vs. 27%; P=0.049) rejection rate. Steroid-resistant rejections were decreased with ATG (3%) vs. basiliximab (14%) (P=0.01). In a univariate regression analysis, basiliximab induction was a risk factor for rejection (HR, 7.1; CI, 3.8-13). No differences were observed regarding complications and graft function up to 5 years. ATG versus basiliximab therapy resulted in identical 1-year (90% vs. 93%), 3-year (87% vs. 89%), and 5-year (78% vs. 83%) pancreas survival (P=0.7). No difference was observed in kidney survival after 1 year (99% vs. 98%), 3 years (97% vs. 98%), and 5 years (95% vs. 95%) (P=0.4). CONCLUSIONS: ATG versus basiliximab induction therapy results in decreased acute cellular rejection in the first year after SPK with similar side effects. Long-term graft function and survival are not affected by induction regimen.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".