Center practice drives variation in choice of US kidney transplant induction therapy: a retrospective analysis of contemporary practice
Bibliographic record
Abstract
To assess factors that influence the choice of induction regimen in contemporary kidney transplantation, we examined center-identified, national transplant registry data for 166 776 US recipients (2005-2014). Bilevel hierarchical models were constructed, wherein use of each regimen was compared pairwise with use of interleukin-2 receptor blocking antibodies (IL2rAb). Overall, 82% of patients received induction, including thymoglobulin (TMG, 46%), IL2rAb (22%), alemtuzumab (ALEM, 13%), and other agents (1%). However, proportions of patients receiving induction varied widely across centers (0-100%). Recipients of living donor transplants and self-pay patients were less likely to receive induction treatment. Clinical factors associated with use of TMG or ALEM (vs. IL2rAb) included age, black race, sensitization, retransplant status, nonstandard deceased donor, and delayed graft function. However, these characteristics explained only 10-33% of observed variation. Based on intraclass correlation analysis, "center effect" explained most of the variation in TMG (58%), ALEM (66%), other (51%), and no induction (58%) use. Median odds ratios generated from case-factor adjusted models (7.66-11.19) also supported large differences in the likelihood of induction choices between centers. The wide variation in induction therapy choice across US transplant centers is not dominantly explained by differences in patient or donor characteristics; rather, it reflects center choice and practice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".