A Physical Head and Neck Surrogate Model to Investigate Blast-Induced Mild Traumatic Brain Injury
Bibliographic record
Abstract
Mild traumatic brain injury (mTBI) resulting from the exposure to a blast shock wave is a challenging problem due to the broad long-term neurological deficits on the victims. The blast-related injury is not only due to the prevalence of military conflicts, but also due to increase terrorist attacks and domestic/industrial accidents. Mechanisms of blast-induced mild traumatic brain injury (BImTBI) have been controversial for a long time and nowadays are one of the most attentive topics among the neurotrauma researches. A physical head and neck model (PHNM) equipped with a surrogate gel brain was developed, and its dynamic responses to a blast wave were evaluated using a predesigned compressed air-driven shock tube. The neck model was constructed and tuned to simulate the actual human neck stiffness. The history of intracranial pressure (ICP) at different locations within the brain was monitored with four miniature pressure transducers. The acceleration of the head as well as the brain model was recorded using two accelerometers mounted internally and outside the PHNM. The shockwave effects on the PHNM were examined at different distance orientations from shock tube exit. The PHNM was exposed to free-field blast tests with controlled reliable positive peak pressure (PPP). The ICP amplitudes/profiles and the acceleration results vary according to the PHNM locations and orientations with respect to shock tube exit. The most vital parameters of ICP wave profiles including PPP, positive phase duration, and positive impulse values were greatly affected by the PHNM locations/orientations from the shock tube exit.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".