Efficacy and safety of CounterFlow in animal models of hemorrhage
Bibliographic record
Abstract
Introduction: Hemorrhage is a major cause of battlefield mortality, contributing to 91% of potentially survivable combat-related deaths. Death from hemorrhage often occurs in pre-hospital settings, and more efficacious hemorrhage control interventions are needed to extend survival and enable casualties to reach definitive surgery. CounterFlow is a novel topical hemostatic agent that uses self-propelling particles to deliver thrombin and tranexamic acid against active bleeding. It achieves and maintains hemostasis at the site of injury through a combination of mechanical, biological, and chemical effects. Methods: All literature in which CounterFlow was tested in animal models of hemorrhage was reviewed to compile its preclinical safety and efficacy as a hemostatic agent. Results: CounterFlow extended survival and halted hemorrhage in multiple animal models that mimicked common and deadly injuries encountered in military and civilian settings, including junctional wounds, surgical bleeding, non-compressible intra-abdominal hemorrhage, and upper gastrointestinal bleeding. Thromboembolism, tissue damage, and toxicity were not observed. Discussion: This narrative review discusses the animal models that have been used to investigate CounterFlow as a hemostatic agent for pre-hospital hemorrhage control. Future directions and potential expansion for civilian use are also considered.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".