P‐TS‐44 | Improving Blood Availability in Low‐ and Middle‐Income Countries through Drone‐Based Blood Delivery: Conclusions from an International Working Group.
Bibliographic record
Abstract
Millions live without access to sufficient blood transfusion services in remote regions of low-and-middle-income countries (LMICs), effectively creating “blood deserts.” The traditional blood banking system is logistically complex and expensive to implement in rural settings, requiring considerable transfusion-specific infrastructure and workforce, and reliant on road travel. Where innovative, multidisciplinary approaches are needed in blood deserts, drone-based blood delivery can aid in streamlining the infrastructure needs and reduce transport time. To explore the potential of drone-based blood delivery for blood transfusions in LMIC blood deserts, a white paper was developed focusing on the current applications of this technology, identifying knowledge gaps, and implementation considerations including future arenas for research and policy. Six international experts (“delegates”) in surgery, transfusion medicine, and public policy were convened as a part of the Innovative Blood Transfusion Strategies for Blood Deserts in Low- and Middle-Income Countries Radcliffe Seminar. The exploratory process included guided reviews of peer-reviewed and gray literature, three group discussions with delegates, seven individual interviews with delegates and additional experts, and a large group discussion with the remaining sixteen delegates during the Seminar. We summarized the current state of the field and existing technologies after extensive research and recognized that while there are smaller companies entering this space, Zipline is the most widely recognized currently. Several factors including geographical and regulatory guidelines, public-private partnerships, and infrastructure would require consideration for successful implementation. Knowledge gaps and barriers namely blood availability, community engagement, and capital investment were identified and to overcome these challenges an implementation plan was laid prioritizing important areas for future study, advocacy, and legislation. Innovative strategies are urgently needed to address the lack of blood transfusion services in LMIC blood deserts. The white paper provides an outline for addressing the challenges associated and leveraging the potential benefits of drone-based delivery to improve blood delivery.
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".