Self-Propelled Dressings Containing Thrombin and Tranexamic Acid Improve Short-Term Survival in a Swine Model of Lethal Junctional Hemorrhage
Bibliographic record
Abstract
Hemorrhage is the leading cause of preventable death in trauma, and hemorrhage from noncompressible junctional anatomic sites is particularly difficult to control. The current standard is QuikClot Combat Gauze packing, which requires 3 min of compression. We have created a novel dressing with calcium carbonate microparticles that can disperse and self-propel upstream against flowing blood. We loaded these microparticles with thrombin and tranexamic acid and tested their efficacy in a swine arterial bleeding model without wound compression. Anesthetized immature female swine received 5 mm femoral arteriotomies to induce severe junctional hemorrhage. Wounds were packed with kaolin-based QuikClot Combat Gauze (KG), propelled thrombin-microparticles with protonated tranexamic acid (PTG), or a non-propelling formulation of the same thrombin-microparticles with non-protonated tranexamic acid (NPTG). Wounds were not compressed after packing. Each animal then received one 15 mL/kg bolus of hydroxyethyl starch solution followed by Lactated Ringer as needed for hypotension (maximum: 100 mL/kg) for up to 3 h. Survival was improved with PTG (3-h survival: 8/8, 100%) compared with KG (3/8, 37.5%) and NPTG (2/8, 25%) (P <0.01). PTG animals maintained lower serum lactate and higher hemoglobin concentrations than NPTG (P <0.05) suggesting PTG decreased severity of subsequent hemorrhagic shock. However, total blood loss, Lactated Ringer infusion volumes, and mean arterial pressures of surviving animals were not different between groups (P >0.05). Thus, in this swine model of junctional arterial hemorrhage, gauze with self-propelled, prothrombotic microparticles improved survival and 2 indicators of hemorrhagic shock when applied without compression, suggesting this capability may enable better treatment of non-compressible junctional wounds.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".