The influence of impact force redistribution and redirection on maximum principal strain for helmeted head impacts
Bibliographic record
Abstract
Sporting helmets with linear attenuating strategies are proficient at reducing the risk of traumatic brain injury. However, the continued high incidence of concussion in American football, has led researchers to investigate novel helmet liner strategies. These strategies typically supplement existing technologies by adding or integrating head-helmet decoupling mechanisms. Decoupling strategies aim to redirect or redistribute impact force around the head, reducing impact energy transferred to the brain. This results in decreased brain tissue strain, which is beneficial in injury risk reduction due to the link between tissue strain and concussive injury. The purpose of this study was to mathematically demonstrate the effect of ten cases, representing theoretical redirection and redistribution helmet liner strategies, on brain tissue strain resulting from impacts to the head. The kinematic response data from twenty head impacts collected in the laboratory was mathematically modified to represent the altered response of the ten different cases and used as input parameters to determine the effect on maximum principal strain (MPS) values, calculated using finite element modeling. The results showed that a reduced dominant coordinate component (contributes the greatest to resultant) of rotational acceleration decreased maximum principal strain in American football helmets. The study theoretically demonstrates that liner strategies, if applied correctly, can influence brain motion, reduce brain tissue strain, and could decrease injury risk in an American football helmet.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".