Bibliographic record
Abstract
An evolving strategy known as kidney paired donation (KPD) matches one donor-candidate pair to another pair with a complementary incompatibility, such that the donor of the first pair donates to the candidate of the second, and vice versa. In Chapter II, we propose novel organ allocation strategies to arrange kidney exchanges under uncertainties with advantages, including (i) allowance for a general utility-based evaluation of potential kidney transplants and an explicit consideration of stochastic features inherent in a KPD program; and (ii) exploitation of possible alternative exchanges when the originally planned allocation cannot be fully executed. This allocation strategy is implemented using an integer programming (IP) formulation, and its implication is assessed via a data-based simulation system by tracking an evolving KPD program over a series of match runs. Extensive simulation studies are provided to illustrate our proposed approach. In recent years, KPD has also been extended to include living non-directed donors, or altruistic donors. In Chapter III, we propose a strategy to sequentially allocate the altruistic donor (or bridge donor) so as to maximize the expected utility over a certain given number of moves. Analogous to the way a computer plays chess, the idea is to evaluate different allocations for each altruistic donor (or bridge donor) by looking several moves ahead along a derived look-ahead search tree. Simulation studies are provided to illustrate our proposed method. In Chapter IV, we examine in greater detail two important aspects in the management of a KPD program --- robust organ allocations and operational uncertainties. We first extend the idea of using exchange sets for planning organ allocations; the outcome of this extension is a more robust allocation strategy derived from the concept of strongly connected components (SCCs). We then study more carefully the operational uncertainties inherent in the management of KPD programs. We also develop a general mathematical framework based on Markov decision processes (MDPs), which can be use to rigorously and systematically formulate the problem of managing KPD programs in the presence of altruistic donors. Finally, we conclude with some discussion and future directions of this dissertation work.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".