Operations Research to Solve Kidney Allocation Problems: A Scoping Review
Bibliographic record
Abstract
Background: In the context of kidney transplantation, health, compatibility, and availability of organs need to be considered to allocate kidneys. Operations research enables health care administrators to optimize resource allocation problems (e.g., kidney allocation for kidney transplant) as well as scheduling problems (staff scheduling and patient scheduling). Operations research employs various techniques incorporating both soft and hard constraints in the equations. The goal is to reduce the gap between demand and supply using advance organ allocation systems and enable adequate access. This paper aims to synthesis the evidence on the use of operations research for allocating deceased donor organs. We also assessed the quality of published studies. Methods: We searched MEDLINE and EMBASE databases from 1946 up to May 2022 without any restrictions. We included studies that explore the methods about the distribution of kidneys from a deceased donor using operations research methods for the conflict resolution if they explore the optimal threshold level to accept/reject a transplant and optimal kidney acceptance strategies for stochastically arriving organs. One investigator (NS) screened each title and abstract and reviewed full text articles and abstracted the data from eligible studies. Results: This scoping review included three published studies that employed operations research techniques. Ahn and Hornberger et al. developed a semi Markov model with five states while examining minimal threshold level of accepting and rejecting the kidney based on QALY index based patient specific ratings. Later on, the sequential stochastic assignment model was developed by Su and Zenios in 2005 and included multiple patients and panelized those who rejected the offer. Stanford et al. suggested a blood type compatible queuing model for stochastically arriving kidneys from deceased donors. Conclusions: The present study is the first to systematically review the operations research methods to manage time and limit wait times with establishing the optimal threshold level to accept/reject a transplant and optimal kidney acceptance strategies for stochastically arriving organs.
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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 | 0.029 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".