The Medical Costs of Determining Eligibility and Waiting for a Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Recent efforts to increase access to kidney transplant (KTx) in the United States include increasing referrals to transplant programs, leading to more pretransplant services. Transplant programs reconcile the costs of these services through the Organ Acquisition Cost Center (OACC). OBJECTIVE: The aim of this study was to determine the costs associated with pretransplant services by applying microeconomic methods to OACC costs reported by transplant hospitals. RESEARCH DESIGN, SUBJECTS, AND MEASURES: For all US adult kidney transplant hospitals from 2013 through 2018 (n=193), we crosslinked the total OACC costs (at the hospital-fiscal year level) to proxy measures of volumes of pretransplant services. We used a multiple-output cost function, regressing total OACC costs against proxy measures for volumes of pretransplant services and adjusting for patient characteristics, to calculate the marginal cost of each pretransplant service. RESULTS: Over 1015 adult hospital-years, median OACC costs attributable to the pretransplant services were $5 million. Marginal costs for the pretransplant services were: initial transplant evaluation, $9k per waitlist addition; waitlist management, $2k per patient-year on the waitlist; deceased donor offer management, $1k per offer; living donor evaluation, procurement and follow-up: $26k per living donor. Longer time on dialysis among patients added to the waitlist was associated with higher OACC costs at the transplant hospital. CONCLUSIONS: To achieve the policy goals of more access to KTx, sufficient funding is needed to support the increase in volume of pretransplant services. Future studies should assess the relative value of each service and explore ways to enhance efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".