Impact of Extreme Temperatures on Hemostatic Gauze Using Thromboelastography
Bibliographic record
Abstract
Introduction Hemorrhage control in austere environments is challenging, particularly for wounds that are not amenable to tourniquets. Hemostatic gauzes are crucial in such settings, but their efficacy may be compromised by suboptimal storage conditions, including extreme temperatures, where discoloration has been observed. This study evaluated the impact of extreme temperature exposure on the efficacy of hemostatic gauze using thromboelastography. Methods Blood from 30 healthy adults was diluted by 30% with hetastarch to mimic trauma-induced coagulopathy. Kerlix and QuikClot Combat Gauze stored for 3 weeks in cold (−10°C), hot (70°C), and room-temperature (22°C) environments were compared in the thromboelastography parameters of R (time to initiation of clot formation), K (clot amplification), α angle (clot formation rate), and MA (maximum amplitude of clot). Results Compared with whole blood, diluted blood had weaker clots with slower clot-formation kinetics ( MA =58 vs 43 mm, P <0.0001; K =2.6 vs 4.0 min, P <0.0001; α angle=55 vs 47 degrees, P <0.0003) but faster clot initiation times ( R =8.7 vs 7.1 min, P <0.0001). Addition of either gauze shortened clot initiation times (Kerlix: 7.1 vs 5.0 min, P <0.0001; QuikClot Combat Gauze: 7.1 vs 2.7 min, P <0.0001), with QuikClot Combat Gauze significantly shortening R compared with Kerlix. Reductions in R values were consistent across temperature extremes ( P <0.05). The other parameters were consistently unaffected ( P >0.05). Conclusions This in vitro laboratory study demonstrated that hemostatic gauze retained its ability to initiate clotting in vitro even after prolonged exposure to temperature extremes.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".