Variations in Center-level Practices Across US Transplant Centers Are Associated With Time to Relisting and Retransplantation in Patients With Graft Failure
Bibliographic record
Abstract
Rationale & Objective: There is known variability in the management of kidney transplant recipients facing graft failure. We hypothesized that variations in the timing of care transitions, immunosuppression weaning, and re-evaluation processes would be associated with differential access to retransplantation and relisting. Study Design: An observational study. Setting & Participants: Survey directed at medical directors of US transplant centers. Exposures: Transplant center-reported practices. Outcomes: Time to retransplantation (and secondarily, relisting) after graft failure. Analytical Approach: Adjusted proportional hazards models with clustering by transplant center. Results: Of the 178 surveyed centers, 77 unique transplant centers (43%) responded. Respondents reported significant variability in the timing of transition of patients back to general nephrologists (ranging from within 1 year of transplantation to never), weaning of immunosuppression with graft failure, and approach to assessments of frailty and adherence during the evaluation for relisting. Transplant centers that transitioned patients back to general nephrologists >3 to <5 years after transplant had lower likelihood of retransplantation (hazard ratio [HR], 0.80; 95% confidence interval [CI], 0.73-0.88) and relisting (HR, 0.80; 95% CI, 0.75-0.85) compared with centers that transitioned patients earlier (between 1-3 years of transplantation). Transplant centers that did not oversee weaning of immunosuppression after graft failure had patients with a lower likelihood of retransplantation (HR, 0.89; 95% CI, 0.79-0.99) and relisting (HR, 0.88; 95% CI, 0.82-0.95) compared with centers that oversaw this weaning. Withdrawal of immunosuppression 12-24 months after return to dialysis was associated with a higher likelihood of retransplantation (HR, 1.28; 95% CI, 1.14-1.43) and relisting (HR, 1.15; 95% CI, 1.06-1.26) compared with withdrawal of immunosuppression within 6 months of graft failure. Limitations: Observational nature of data and potential for residual confounding. Conclusions: There is significant variation in the management of patients with graft failure across US transplant centers during the transition of care, and this variation was associated with differential access of patients to retransplantation and relisting.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".