The estimated negative impacts on the red blood cell inventory of reducing shelf‐life at two large health authorities in British Columbia, Canada, using a discrete‐event simulation model
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Reducing the maximum red blood cell (RBC) shelf-life is under consideration due to potential negative effects of older blood. An assessment of the impacts of this change on blood supply chain management is evaluated. MATERIALS AND METHODS: We performed a simulation study using data from 2017 to 2018 to estimate the outdate rate (ODR), STAT order and non-group-specific RBC transfusion at two Canadian health authorities (HAs). RESULTS: Shortening shelf-life from 42 to 35 and 28 days led to the following: ODRs (in percentage) in both HAs increased from 0.52% (95% confidence interval [CI] 0.50-0.54) to 1.32% (95% CI 1.26-1.38) and 5.47% (95% CI 5.34-5.60), respectively (p < 0.05). The estimated yearly median of outdated RBCs increased from 220 (interquartile range [IQR] 199-242) to 549 (IQR 530-576) and 2422 (IQR 2308-2470), respectively (p < 0.05). The median number of outdated redistributed units increased from 152 (IQR 136-168) to 356 (IQR 331-369) and 1644 (IQR 1591-1741), respectively (p < 0.05). The majority of outdated RBC units were from redistributed units rather than units ordered from the blood supplier. The estimated weekly mean STAT orders increased from 11.4 (95% CI 11.2-11.5) to 14.1 (95% CI 13.1-14.3) and 20.9 (95% CI 20.6-21.1), respectively (p < 0.001). The non-group-specific RBC transfusion rate increased from 4.7% (95% CI 4.6-4.8) to 8.1% (95% CI 7.9-8.3) and 15.6% (95% CI 15.3-16.4), respectively (p < 0.001). Changes in ordering schedules, decreased inventory levels and fresher blood received simulated minimally mitigated these impacts. CONCLUSION: Decreasing RBC shelf-life negatively impacted RBC inventory management, including increasing RBC outdating and STAT orders, which supply modifications minimally mitigate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".