Evaluation of Hemostatic Effectiveness in a Standard Swine Hemorrhage Model of Severe Bleeding: A Comparative Study of Chitosan Gauze and Kaolin Gauze
Bibliographic record
Abstract
Hemorrhage remains a critical factor contributing to avoidable fatalities in combat scenarios and civilian emergencies. Significant advancements have been made in managing extremity hemorrhage through tourniquet application, although not all injuries can be effectively treated with this method. In the United States, the Committee on Tactical Combat Casualty Care (CoTCCC) has emphasized the importance of employing hemostatic dressings during initial trauma care as an alternative when tourniquet application is not feasible. Similar recommendations have been adopted globally, highlighting the pivotal role of topical hemostats in managing such critical situations. Among the recommended topical hemostats, hemostatic gauze, composed of biomaterials including chitosan and kaolin, has demonstrated superior efficacy in rapidly achieving hemostasis. The objective of this study was to evaluate the hemostatic efficacy of axiostat gauze (AG) in an extremity arterial hemorrhage model in swine and compare it with QuikClot Combat Gauze (QCG). The femoral artery was selected to create a model of severe arterial hemorrhage. This model is widely accepted as a standardized combat injury model by military laboratories, including those of the US Department of Defense. The findings indicate that AG has a superior safety profile, evidenced by the absence of particle release, and offers advantages in terms of faster hemostasis, reduced overall blood loss, and greater fluid absorbency compared to QCG. Although AG achieved quicker hemostasis, both AG and QCG ultimately provided effective eventual hemostasis in all animals, demonstrating comparable overall efficacy. In addition to its hemostatic performance, AG also possesses antimicrobial properties and is biocompatible. These attributes make AG a highly effective option for managing severe traumatic bleeding. Thus, AG can be recommended for use in both military and civilian pre-hospital settings as a strategic solution for critical bleeding situations.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".