Variation in Structure and Delivery of Care Between Kidney Transplant Centers in the United States
Bibliographic record
Abstract
Although the United States possesses one of the most comprehensive transplant registries in the world, nationally representative data on how transplant care is structured and delivered is lacking. Therefore, we surveyed all 208 adult kidney transplant centers in the United States, excluding 37 pediatric and 58 inactive adult centers. Respondents were asked about the characteristics of their kidney transplant programs (25 items), the structure and process of care (18 items), coordination of care (10 items), and the characteristics of transplant physicians and surgeons (9 items). The survey was completed by directors of 156 transplant centers (75% response). The results demonstrated significant variation between centers in several domains. Sixty-five percent of transplant centers do not have a dedicated transplant pharmacist in outpatient care. Two thirds of transplant centers do not see the kidney transplant recipients at least monthly during the first year. Less than 30% of centers perform joint sit-down or walking rounds between nephrology and transplant surgery. There was significant variation in the structure and process of care in kidney transplantation. This implies variation in the use of resources at the transplant centers. This variation should be studied to determine best practices associated with optimal kidney allograft and patient survival.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".