Freeze‐dried plasma: Hemostasis and biophysical analyses for damage control resuscitation
Bibliographic record
Abstract
BACKGROUND: Effective hemorrhage protocols prioritize immediate hemostatic resuscitation to manage hemorrhagic shock. Prehospital resuscitation using blood products, such as whole blood or alternatively dried plasma in its absence, has the potential to improve outcomes in hemorrhagic shock patients. However, integrating blood products into prehospital care poses substantial logistical challenges due to issues with storage, transport, and administration in field environments. STUDY DESIGN AND METHODS: We utilized hemostatic assays and advanced biophysical techniques, such as calorimetry, infrared spectoscopy, dynamic light scattering, and biolayer interferometry, to compare the functional and structural properties of freeze-dried plasma (FDP; OctaplasLG Powder, Octapharma AB) with those of fresh plasma controls. RESULTS: Hemostatic characterization of FDP revealed that clot formation properties and coagulation parameters were largely comparable to fresh plasma controls, with some variations observed in Von Willebrand factor-ADAMTS13 axis and fibrinolysis. No change to moisture content of FDP (~1% water content) was observed after 6-month storage at ambient conditions. Biophysical analyses of FDP during transfusion demonstrated spontaneous exothermic mixing of FDP in plasma, a dilution effect from saline, as well as comparable stability to plasma controls. Quantification of ligand-binding affinities of platelet receptors activated GPIIbIIIa and GPIbα showed comparable binding properties to plasma controls. CONCLUSION: Our results show that FDP exhibits hemostatic functionality and protein stability on par with fresh plasma, as assessed by novel, highly sensitive techniques. FDP therefore represents a viable alternative to conventional plasma in damage control resuscitation, offering significant logistical and storage advantages for prehospital and remote applications, especially in scenarios where whole blood is unavailable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".