Shielding the Skull: Exploring the Influence of Facial Protection, Impact Location and Neck Stiffness on Hockey Helmet Safety During a Linear Impact
Bibliographic record
Abstract
Originally designed to mitigate skull fractures and traumatic brain injuries in hockey players, hockey helmets have now become a critical focus for further research due to the rise in mild traumatic brain injuries. With the sport's evolution introducing stronger and faster players, new approaches that incorporate facial shielding in helmet technology and enhance athletes’ neck strength are needed to reduce concussion risks. This study pursued two primary objectives. Firstly, it sought to determine if a hockey helmet's stiffness fluctuated at different contact locations during static compression with the inclusion of facial shielding. Secondly, it examined the influence of impact location, facial protection type, and neck stiffness on head injury risk during simulated dynamic impacts, gauged by the Gadd Severity Index (GSI). The findings revealed that helmet stiffness varied across locations, and a significant three-way interaction was observed between facial shielding, impact location, and neckform stiffness level concerning GSI measures at p < 0.05. Further analysis unveiled significant two-way interactions between impact location and facial shielding across neck strength levels at p < 0.05. These outcomes underscore the critical role of facial shielding, neck strength and impact location, in averting brain injuries in hockey. The results carry practical implications for helmet manufacturers, standards bodies, coaches, and players, urging a comprehensive approach to helmet design and player safety.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".