POS-744 CLINICAL CORRELATES AND OUTCOMES OF DUAL BASILIXIMAB AND THYMOGLOBULIN INDUCTION IN KIDNEY TRANSPLANT RECIPIENTS: A NATIONAL STUDY
Bibliographic record
Abstract
The unplanned use of dual induction therapy with interleukin-2 receptor blocking antibodies (IL2rAb) and thymoglobulin (TMG) may portend adverse clinical outcomes compared to either therapy alone. We used national transplant registry data to study clinical correlates and outcomes of single versus dual induction therapy in adult kidney-only transplant recipients in the United States (2005-2018). The risk of death and graft loss at 1 and 5 years according to induction therapy type was assessed using multivariate Cox regression analysis (LCLaHRUCL, adjusted hazard ratio with 95% upper and lower confidence limits). Of the 157,351 adult kidney-only transplant recipients included in the study, 67% were treated with TMG alone, 29% were treated with IL2rAb alone, and 5% were treated with both IL2rAb and TMG. Compared with IL2rAb alone induction, the strongest correlates of IL2rAb and TMG induction included Black race, calculated panel reactive antibody (cPRA) ≥80%, prednisone-sparing maintenance immunosuppression, more recent transplant eras, longer cold ischemia time, and delayed graft function. Compared with induction with TMG alone, dual induction therapy was associated with an increased 5-year risk of death (aHR 1.071.151.23; P<0.0001), death-censored graft failure (aHR 1.051.131.22; P<0.05), and all-cause graft failure (aHR 1.061.121.18; P<0.0001). Approximately 15% of kidney transplant patients who received IL2rAb induction also received TMG induction and these recipients had an increased risk of death and graft loss compared with those who received TMG alone. Further research is needed to develop risk prediction tools to guide a safe and optimal induction protocol for kidney 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".