Innovative blood transfusion strategies to address global blood deserts: a consensus statement from the Blood Delivery via Emerging Strategies for Emergency Remote Transfusion (Blood DESERT) Coalition
Bibliographic record
Abstract
In rural settings worldwide, many people live in effective blood deserts without access to any blood transfusion. The traditional system of blood banking is logistically complex and expensive for many resource-restricted settings and demands innovative and multidisciplinary solutions. 17 international experts in medicine, industry, and policy participated in an exploratory process with a 2-day hybrid seminar centred on three promising innovative strategies for blood transfusions in blood deserts: civilian walking blood banks, intraoperative autotransfusion, and drone-based blood delivery. Participant working groups conducted literature reviews and interviews to develop three white papers focused on the current state and knowledge gaps of each innovation. Seminar discussion focused on defining blood deserts and developing innovation-specific implementation agendas with key research and policy priorities for future work. Moving forward, advocates should prioritise the identification of blood deserts and address the context-specific challenges for these innovations to alleviate the ongoing crisis in blood deserts.
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".