In Situ Intracranial Strain Measurements within an Elastomeric Brain Surrogate
Bibliographic record
Abstract
Incidence of concussion remains high despite the widespread use of helmets.While the primary cause of mild traumatic brain injury (mTBI) is thought to be intracranial strain, current helmet evaluation techniques resolve head kinematics as the primary evaluation metrics.These techniques have been highly effective in reducing focal brain injuries.However, their effectiveness in reducing the incidence or severity of concussion has been less clear.There remains a need to advance tools and methodologies capable of making a more direct link between helmet certification protocols and the causes of concussive injury.This study presents displacement and strain within a deformable head surrogate, the BIPED headform, subjected to an extensive series of impacts.Impacts were captured under high speed X-ray at 5,000 fps, and strain fields were computed using digital image correlation.Results from this study were compared to cadaveric brain tissue displacements measured under similar impact experiments.iii As I reflect on my time at Carleton, I'd like to take a moment to acknowledge the incredible group of people who have made this chapter in my life possible.Firstly, this work would not have been possible without the knowledge and support of my supervisor, Dr. Oren Petel.You have pushed me to grow in ways unimaginable, and with your guidance and mentorship, I have found a space where I truly feel excited about the work that I have, and will continue to, put forward.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".