An In Vitro Experimental Model for Investigating Aortic Pressure Dynamics Under Blunt Thoracic Impacts
Bibliographic record
Abstract
Blunt traumatic aortic rupture (BTAR) is a life-threatening injury that can occur in high-impact events such as motor vehicle collisions, falls, and sports-related trauma involving the thorax. Despite improvements in vehicle safety features and regulations by using anthropometric test devices, BTAR remains associated with substantial clinical severity and high mortality, and its underlying rupture mechanisms are still poorly understood. We developed a novel proof of concept for a human-thorax surrogate for in vitro crash testing comprising a pulsatile heart pump, anatomically shaped silicone aorta, 3D-printed rib cage, and ballistic gel damping layer to investigate the fluid mechanics response to thoracic impact. The cardiovascular mock circulatory loop system of this surrogate was validated by obtaining physiological pressure waveforms with 120/80 mmHg of pressure and an average flowrate of 5.18 L/min. Subsequently, impacts were delivered to the sternum using a standardized pendulum system commonly employed in crash test dummy calibration. Impact severity was modulated by varying the pendulum's release height, corresponding to different kinetic energy levels. Instantaneous aortic pressure waveforms were recorded before, during, and after impact. The results demonstrate that thoracic impacts induce sharp, transient alterations in aortic pressure magnitude, with greater severity observed at higher energy levels, reaching a peak of aortic pressure of 287.01 mmHg. This experimental approach provides reproducible and physiologically relevant conditions for studying BTAR and offers valuable insights into the mechanisms underlying aortic rupture, which may guide the design of improved prevention and protection strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".