Necessity Is the Mother of Invention: Advocating for Alternatives to Banked Blood
Bibliographic record
Abstract
Safe blood is a scarce resource, especially in low- and middle-income countries. Over 50% of blood is donated and utilized in high-income countries such as the United States, Canada, Europe and Australia. Although the number of hemorrhage-related deaths each year is unknown, at least 5 million people annually die from trauma-related injuries, overwhelmingly due to hemorrhage. While all blood transfusions carry the potential risk of a transfusion transmissible infection, the unavailability of blood in hemorrhagic emergencies results in almost certain death. Here, we outline three potential domains for interventions: enhancing delivery systems, increasing salvage of blood in operating rooms, and walking blood banks. To truly scale surgical systems and meet the needs of the patients in those systems, it is imperative that we increase access to safe blood now.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".