Potential military applications for a new freeze‐dried plasma
Bibliographic record
Abstract
Hemorrhage is a leading cause of potentially preventable death in both military and civilian trauma. Current resuscitation approaches minimize crystalloids and emphasize plasma and other blood components to achieve a balanced transfusion as early as possible after injury. Owing to the nature of military operations, military medical systems must contend with great distances, degraded infrastructure, and harsh environments, as well as combat and humanitarian assistance and disaster relief (HADR) scenarios. These factors limit both patient movement and the ability to deliver blood products to the point of need. Current projections are that future military scenarios will have longer times to reach a medical treatment facility than experienced in recent conflicts, increasing the need for logistically efficient blood products. Freeze-dried plasma (FDP) is rapidly available, easy to use, and shelf-stable at room temperature, making it easier to deliver at the point of need in challenging military environments. For the past 30 years, FDP has been available in only a few countries. Where it has been available, it has become the preferred plasma for austere or military expeditionary settings. Recently, a new FDP, OctaplasLG Powder, was approved in 17 countries worldwide and for emergency use by the Canadian and United States militaries. It is expected that FDP will soon become available to many more militaries. This review discusses the importance of plasma, reassesses the potential military uses of FDP across the range of military operations, and provides a brief discussion of OctaplasLG Powder.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".