Reducing the age of transfused red blood cells in hospitals: ordering and allocation policies
Bibliographic record
Abstract
BACKGROUND: Although recent randomized controlled trials have not found increased risk of morbidity/mortality with older red blood cells (RBCs), several large trials will be completed soon providing power to detect smaller risks if indeed they exist. Hence, there may still be a need for inventory management policies that could reduce the age of transfused RBCs without compromising availability or resulting in excessive outdates. MATERIALS AND METHODS: We developed a computer simulation model based on data from an acute care hospital in Hamilton, Ontario. We evaluated and compared the performance of certain practical ordering and allocation policies in terms of outdate rate, shortage rate and the distribution of the age of issued RBCs. RESULTS: During the 1-year period for which we analysed the data, 10349 RBC units were transfused with an average issue age of 20·7 days and six units were outdated (outdate rate: 0·06%). Adopting a strict first in, first out (FIFO) allocation policy and an order-up-to ordering policy with target levels set to five times the estimated daily demand for each blood type, reduced the average issue age by 29·4% (to 14·6 days), without an increase in the outdate rate (0·05%) or resulting in any unmet demand. Further reduction of issue age without a significant increase in outdate rate was observed when adopting non-FIFO threshold-based allocation policies and appropriately adjusting the order-up-to levels. CONCLUSION: A significant reduction of issue age could be possible, without compromising availability or resulting in excessive outdates, by properly adjusting the ordering and allocation policies at the hospital level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".