The effect of a novel impact management strategy on maximum principal strain for reconstructions of American football concussive events
Bibliographic record
Abstract
The purpose of this study was to test the effectiveness of a helmet to reduce maximum principal strain by decoupling the head–helmet interface in reconstructions of head-to-head concussive impacts in American football. The primary goal of the American football helmet has been protection of players against skull fractures and other traumatic brain injuries. The modern-day helmet has evolved and been designed to mitigate traumatic brain injury, but it does not offer optimized protection against concussive injury. Studies to determine the influence of decoupling strategies on changes to brain motion and the resulting influence on maximum principal strain, a metric associated with concussive injury, have not yet been examined under concussive impact conditions. In this study, 19 helmet-to-helmet concussive events from professional American football were reconstructed using a pneumatically driven linear impactor to determine the components (resultant and dominant) of linear and rotational acceleration. The University of College Dublin Brain Trauma Model was used to determine maximum principal strain values. A prototype helmet with a decoupling liner strategy was shown to significantly reduce maximum principal strain in seven of the head-to-head concussive impact reconstructions. In each case, the prototype helmet significantly reduced the dominant coordinate component of rotational acceleration. The results of this study indicate the potential of a helmet liner decoupling system in reducing maximum principal strain for reconstructions of concussive events in American football.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".