Protective Headgear Attenuates Forces on the Inner Table and Pressure in the Brain Parenchyma During Blast and Impact: An Experimental Study Using a Simulant-Based Surrogate Model of the Human Head
Bibliographic record
Abstract
Military personnel sustain head and brain injuries as a result of ballistic, blast, and blunt impact threats. Combat helmets are meant to protect the heads of these personnel during injury events. Studies show peak kinematics and kinetics are attenuated using protective headgear during impacts; however, there is limited experimental biomechanical literature that examines whether or not helmets mitigate peak mechanics delivered to the head and brain during blast. While the mechanical links between blast and brain injury are not universally agreed upon, one hypothesis is that blast energy can be transmitted through the head and into the brain. These transmissions can lead to rapid skull flexure and elevated pressures in the cranial vault, and, therefore, may be relevant in determining injury likelihood. Therefore, it could be argued that assessing a helmet for the ability to mitigate mechanics may be an appropriate paradigm for assessing the potential protective benefits of helmets against blast. In this work, we use a surrogate model of the head and brain to assess whether or not helmets and eye protection can alter mechanical measures during both head-level face-on blast and high forehead blunt impact events. Measurements near the forehead suggest head protection can attenuate brain parenchyma pressures by as much as 49% during blast and 52% during impact, and forces on the inner table of the skull by as much as 80% during blast and 84% during impact, relative to an unprotected head.
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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 | 0.001 |
| Research integrity | 0.000 | 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".