Intracranial Displacements due to Blunt Force Impact in a Postmortem Human Surrogate Brain
Bibliographic record
Abstract
Prevention and diagnosis of traumatic brain injuries (TBI) are reliant on understanding the biomechanical response of the brain to external stimuli. Finite element models (FEM) and artificial head surrogates are becoming a common method of investigating the dynamic response of the brain to injurious impact and inertial stimuli. The accuracy and validity of these models is reliant on postmortem human subject (PMHS) research to produce biofidelic brain tissue responses. Previous PMHS research has been performed to measure intracranial pressures, displacements, and strains when subjected to impact and inertial loading; however, there remains a need for additional PMHS datasets to improve our understanding of the brain’s dynamics. The purpose of this study is to measure the relative brain–skull displacement in a PMHS specimen when subjected to blunt force impacts. A high-speed X-ray (HSXR) imaging system and embedded radiopaque elastomeric markers were used to record PMHS impacts at varying impact velocities for two specimens: specimen CO-108 was subjected to a series of frontal impacts and specimen CO-109 was subjected to a series of rear impacts. Brain–skull relative deformation in each specimen indicates that brain deformation is dependent both on anatomical regions and of impact direction.
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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 | 0.000 | 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".