Screening Strategies for Patients on the Kidney Transplant Waiting List
Bibliographic record
Abstract
Transplant centers screen patients on the kidney transplant waiting list to identify patients with severe conditions such as cardiovascular disease (CVD), which makes them ineligible for a transplant. We propose a model for finding screening strategies, with the objective of minimizing the sum of the expected screening cost and the expected penalty cost associated with transplanting an organ to an ineligible patient. Our results suggest that current screening guidelines, which are based only on patients’ risk for developing CVD, are significantly dominated by policies that also consider factors related to patients’ waiting time. In particular, our numerical experiments based on waiting list data in British Columbia show that compared with the current screening policy, our model-based policy results in a 35.6% reduction in the total annual cost by reducing the percentage of kidneys offered to patients with undetected CVD by 57.4% while using only 5% more screenings per year. The online appendix is available at https://doi.org/10.1287/opre.2017.1632 .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".