Development of a test method for adult ice hockey helmet evaluation
Bibliographic record
Abstract
Ice hockey helmet standards have primarily been developed to reduce risk of traumatic brain injury (TBI). While severe TBI has become a rare event in ice hockey, concussion, a type of mild TBI, remains a common head injury. Concussions, in ice hockey result from a number of head impact events including, collisions, stick impacts, puck impacts, falls into the boards, impacts to the glass, and falls to the ice. Helmet testing methods need to represent the impact events creating concussions in ice hockey. The purpose of this research was to develop a helmet test protocol and performance metric for concussive impacts in ice hockey. A protocol using concussion impact parameters from published literature was created that used monorail and linear impactors to impact a helmeted Hybrid III headform. The linear and rotational acceleration time curves were then used to calculate brain tissue strain using the University College Brain Trauma Model. The proposed test protocols created kinematic responses that were representative of levels associated with concussion in ice hockey. Rotational velocity and rotational acceleration were both identified as useful performance metrics representing levels of risk for concussion.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".