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 The estimated yearly median of outdated RBCs increased from 220 (interquartile range [IQR] 199-242) to 549 (IQR 530-576) and 2422 (IQR 2308-2470), respectively (p 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 The median number of outdated redistributed units increased from 152 (IQR 136-168) to 356 (IQR 331-369) and 1644 (IQR 1591-1741), respectively (p 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 The majority of outdated RBC units were from redistributed units rather than units ordered from the blood supplier.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 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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".