A Study of the Impact Response of Discrete Regions of the Human Cadaver Brain
Bibliographic record
Abstract
The risk of trauma to the brain due to head impacts is high, despite widespread use of protective equipment and injury mitigation efforts.Mitigation techniques are developed through simulations that utilize complex finite element models of the brain and head.Validation of such models is limited, as existing empirical data is sparse.Due to technical constraints, empirical studies have only revealed broad brain tissue deformation.The objective of this study was to develop a comprehensive methodology for measuring the displacement of discrete brain structures during impact.An advanced X-ray system was used to capture brain motion for two cadaveric specimens at 7,500 fps.Displacement of brain structures was determined for 7 impacts on each specimen.Motion trends were region dependent, with some regions exhibiting multi-modal displacement.Displacement of discrete structures including the corpus callosum was measured.These methods will help clarify the response of the brain to impact.iii When I started working on this project in the fall of 2017, I was fairly certain that next two years would be a breeze.I am humbled to report that I was very wrong.The last three years, have been a roller-coaster of excitement, challenges, incredible learning -all of which were wonderful, but none of which were a breeze.Over this time, I have been involved with some exciting work, been to some thrilling places, and experienced the fallout of the 2020 Pandemic.Despite these ups and downs, I have still managed to reach a destination.While I take a moment to reflect on how I got here, I find it necessary to acknowledge the folks who helped make it happen.I must first recognize my co-supervisors, Professors Oren Petel and Hanspeter Frei, who convinced me to join their team years ago.Each of these outstanding professors has boosted my abilities and this work would not be remotely possible without their contributions.I have truly enjoyed working as a colleague on this project and others that may come along in the future.To Oren, thank you for your knowledge, insights, patience, fair treatment, and willingness-to-stay-in-the-lab-until-the-sun-comes-up.These experiences have helped me become a better student, teacher, and researcher.To Hanspeter, if not for your expertise in cadaver work,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".