Threshold-Based Allocation Policies for Inventory Management of Red Blood Cells
Bibliographic record
Abstract
Under current regulations, red blood cell (RBC) units can be transfused to patients up to 42 days after donation. However, recent studies suggest an association between the age of transfused RBCs and adverse clinical outcomes for their recipients. Therefore, there is an interest in inventory management policies that could reduce the age of transfused RBCs without compromising their availability. In this work, we study the performance of a practical family of threshold-based allocation policies, designed to trade off the age of RBC transfusions with their availability at hospitals. To this end, we consider a stylized model of a hospital blood bank that procures its required blood from local donations. For this model, we develop a new method to exactly evaluate the performance of the threshold policy in terms of the distribution of the age of allocated units and the proportion of outdates and lost demand. Through numerical and structural results, we obtain new insights on the performance of the threshold policy and in particular on how it compares with shortening the shelf life of RBCs (e.g., from 42 to 28 days). We verify and discuss the robustness of these results to the model assumptions in a simulation study calibrated using data from a Canadian hospital blood bank. The online appendix is available at https://doi.org/10.1287/msom.2017.0650 .
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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 | 0.008 | 0.026 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".