Abstract 345: Direct Ultrasound-Based Quantification of Bleeding from a Junctional Femoral Artery Wound During Fluid Resuscitation
Bibliographic record
Abstract
Background: Hypotensive resuscitation is a strategy to reduce blood loss after injury even though associations between blood pressure and bleeding remain poorly understood. We used ultrasound to quantify bleeding during fluid resuscitation in a pilot study using a swine bleeding model. We hypothesized that bleeding is associated with central hemodynamics and clot formation during fluid resuscitation. Methods: Immature swine (N=8) were anesthetized and hemorrhagic shock was induced by bleeding from a 5mm femoral arteriotomy. A wound sealing device (iTClamp™) was applied to the wound, sealing the skin. A 15ml/kg bolus of 6% hydroxyethyl starch solution was infused followed by Lactated Ringers to maintain mean arterial pressure (MAP)=60mmHg for up to 3 hours. Hemodynamic and thrombelastographic (TEG) measurements were made. Bleeding was measured using Doppler ultrasound to identify a pulsatile jet of blood within the wound hematoma and mean wound bleeding velocity (WBV) in cm/sec was calculated. Results: Survival was 50% (4/8), and mean blood loss was 34ml/kg in survivors vs. 44ml/kg in nonsurvivors (T test, p<0.001). WBV was significantly and positively associated with MAP (R=0.34, p=0.016) and cardiac output (R=0.43, p=0.002) and WBV tended to be increased in survivors (88 cm/sec vs. nonsurvivors (72cm/sec, p=0.07). The association between MAP and WBV could be impacted by clot strength only when MAP was hypotensive (<50mmHg) (ANOVA interaction effect p=0.004). Maintenance of a contained high-velocity bleeding pocket within a clot-based pseudo aneurysm was seen as a possible mechanism governing the associations between MAP, clot strength, and WBV. Conclusion: This pilot study supports a hypotensive resuscitation strategy to reduce junctional arterial bleeding. Normotensive conditions may reduce the effects of clot-enhancing hemostatic therapy during fluid resuscitation of uncontrolled hemorrhage. ![][1] [1]: /embed/graphic-1.gif
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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 | 0.000 | 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".