Use and Outcomes of Induction Therapy in Well-Matched Kidney Transplant Recipients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The optimal induction treatment in low-immune risk kidney transplant recipients is uncertain. We therefore investigated the use and outcomes of induction immunosuppression in a low-risk cohort of patients who were well matched with their donor at HLA-A, -B, -DR, -DQB1 on the basis of serologic typing. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Our study was an observational study of first adult kidney-only transplant recipients in the United States recorded by the Organ Procurement and Transplant Network. RESULTS: Among 2976 recipients, 57% were treated with T cell-depleting antibodies, 28% were treated with an IL-2 receptor antagonist, and 15% were treated without induction. There was no difference in allograft survival, death-censored graft survival, or death with function between patients treated with an IL-2 receptor antagonist and no induction therapy. In multivariable models, patients treated with T cell-depleting therapy had a similar risk of graft loss from any cause, including death (hazard ratio, 1.19; 95% confidence interval, 0.98 to 1.45), compared with patients treated with an IL-2 receptor antagonist or no induction. The findings were consistent in subgroup analyses of Black recipients, patients grouped by calculated panel reactive antibody, and donor source. The incidence of acute rejection at 1 year was low (≤5%) and did not vary between treatment groups. CONCLUSIONS: Use of induction therapy with T cell-depleting therapy or IL-2 receptor antagonists in first kidney transplant recipients who are well matched with their donor at the HLA-A, -B, -DR, -DQB1 gene loci is not associated with improved post-transplant outcomes.
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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 | 0.003 |
| 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 | 0.001 | 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".