Dried Plasma for Major Trauma: Past, Present, and Future
Bibliographic record
Abstract
Uncontrollable bleeding is recognized as the leading cause of preventable death among trauma patients. Early transfusion of blood products, especially plasma replacing crystalloid and colloid solutions, has been shown to increase survival of severely injured patients. However, the requirements for cold storage and thawing processes prior to transfusion present significant logistical challenges in prehospital and remote areas, resulting in a considerable delay in receiving thawed or liquid plasma, even in hospitals. In contrast, freeze- or spray-dried plasma, which can be massively produced, stockpiled, and stored at room temperature, is easily carried and can be reconstituted for transfusion in minutes, provides a promising alternative. Drawn from history, this paper provides a review of different forms of dried plasma with a focus on in vitro characterization of hemostatic properties, to assess the effects of the drying process, storage conditions in dry form and after reconstitution, their distinct safety and/or efficacy profiles currently in different phases of development, and to discuss the current expectations of these products in the context of recent preclinical and clinical trials. Future research directions are presented as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".