Designing the blood supply chain: how much, how and where?
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The topology of the blood supply chain network can take different forms in different settings, depending on geography, politics, costs, etc. Many developed countries are moving towards centralized networks. The goal for all blood distribution networks, regardless of topology, remains the same: to satisfy demand at minimal cost and minimal wastage. STUDY DESIGN AND METHODS: Mathematically, the blood supply system design can be viewed as a location-allocation problem, where the aim is to find the optimal location of collection and production facilities and to assign hospitals to them to minimize total system cost. However, most location-allocation models in the blood supply chain literature omit several important aspects of the problem, such as selecting amongst differing methods of collection and production. In this paper, we present a location-allocation model that takes these factors into account to support strategic decision-making at different levels of centralization. RESULTS: Our approach is illustrated by a case study (Colombia) to redesign the national blood supply chain under a range of realistic travel time limitations. For each scenario, an optimal supply chain configuration is obtained, together with optimal collection and production strategies. We show that the total costs for the most centralized scenario are around 40% of the costs for the least centralized scenario. CONCLUSION: Centralized systems are more efficient than decentralized systems. However, the latter may be preferred for political or geographical reasons. Our model allows decision-makers to redesign the supply network per local circumstances and determine optimal collection and production strategies that minimize total costs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it