Investigation of an Ice Hockey Helmet Test Protocol Representing Three Concussion Event Types
Bibliographic record
Abstract
Abstract Although ice hockey helmet standards mitigate the risk of catastrophic head injuries, the risk of concussion remains high. To improve protection, helmets need to be evaluated using impact conditions reflecting how concussions occur in ice hockey. The purpose of this research was to evaluate how three helmet impact tests represent three common concussive events in ice hockey. An ice drop test (representing head-to-ice impacts), 30° and 45° anvil boards drop tests (representing head-to-boards impacts), and medium and high shoulder compliance pneumatic ram tests (representing shoulder-to-head impacts) were performed on a hybrid III headform. Finite element analysis using the University College Dublin Brain Trauma Model was conducted to calculate maximum principal strain (MPS). The mean dynamic response and MPS from each helmet test were compared to a dataset of concussive injury reconstructions. Stepwise forward multiple linear regressions identified the dynamic response variables producing the strongest relationship with MPS for each helmet test and concussion reconstructions. The results indicated that the ice and boards drop test and shoulder ram test had magnitudes and relationships between variables similar to the concussion reconstructions. The proposed testing methodologies in this study closely approximated concussion mechanics in ice hockey and inform improved helmet test standards and design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| 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) | 0.000 | 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 teacher head, 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".