Improving transfusion access through improved policy: a call for a less fragmented blood supply
Bibliographic record
Abstract
INTRODUCTION: Fragmentation across operations, data systems, governance, and regulation leaves many blood supply networks ill-equipped to provide timely, equitable, and crisis-resilient transfusion support. Public health emergencies, such as COVID-19 and natural disasters, have exposed the human and economic costs of these structural flaws, and how variability in practice about who can see and share data still impedes coordination even when the overall blood inventory is adequate. AREAS COVERED: This Critical Perspective examines blood supply coordination challenges in high-income countries, focusing on governance structures, operational isolation, regulatory inconsistencies, and data system incompatibilities. We analyze evidence from crisis events including pandemics, natural disasters, and mass casualty incidents to illustrate coordination failures and successful response models. The review synthesizes peer-reviewed literature identified through PubMed searches (January 2010 - September 2025), supplemented by regulatory documents, industry reports, and government policy analyses from blood regulatory agencies in the United States, United Kingdom, Canada, and other high-income countries. EXPERT OPINION: Effective solutions require coordinated interventions across multiple domains rather than isolated or localized improvements. Priority areas include governance structures that enable cross-institutional collaboration, interoperable data systems with standardized sharing protocols, regulatory frameworks that incentivize coordination, and value-based reimbursement models that reward system-wide performance.
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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 | 0.065 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".