In Vitro Evaluation of Real-Time Viscoelastic and Coagulation Properties of Various Classes of Topical Hemostatic Agents Using a Novel Contactless Nondestructive Technology
Bibliographic record
Abstract
Hemorrhaging is the main cause of death among combat and civilian injuries and has significant clinical and economic consequences. Despite their vital roles in bleeding management, an optimal topical hemostatic agent (HA) has yet to be developed for a particular scenario. This is partly due to a lack of an overarching quantitative testing technology to characterize the various classes of HAs in vitro. Herein, the feasibility of a novel, contactless, and nondestructive technique to quantitatively measure the shear storage modulus ( G ′) and clotting properties of whole blood in contact with different dosages of eight topical HAs, including particulates and gauze-like and sponge-like systems, was assessed. The real-time G ′–time profiles of these blood/HA systems revealed their distinct biomechanical behavior to induce and impact coagulation. These were analyzed to characterize the clot initiation time, clotting rate, clotting time, and apparent stiffness of the formed clots (both immediately and temporally), which were correlated with their reported hemostatic mechanisms of action. Moreover, the HAs that worked independently from the natural blood clotting cascade were identified and quantified through this technology. In sum, this study indicated that the nondestructive nature of the technology may offer a promising tool for accurate, quantitative in vitro measurements of the clotting properties of various classes of HAs, which may be used to better predict their in vivo outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".