Event-specific impact test protocol for ice hockey goaltender masks
Bibliographic record
Abstract
Goaltenders in the sport of ice hockey are at high risk for concussions from falls to the ice, player collisions and puck impacts. However, current methods used to certify helmets only consider head accelerations for drop tests which may not describe all common injury mechanisms relating to concussion. The purpose of this study was to describe the characteristics of 3 events associated with concussions for ice hockey goaltenders. A helmeted medium National Operating Committee on Standards for Athletic Equipment (NOCSAE) headform was impacted under conditions representing 3 injury events. Three impact locations' velocities were selected for each event based on video analysis of real-world concussive events. Peak resultant linear acceleration, rotational acceleration and rotational velocity of the headform were measured. The University College Dublin Brain Trauma Model (UCDBTM) was used to calculate maximum principal strain (MPS) and von Mises stress in the cerebrum. Each impact event produced a unique dynamic response and brain stress and strain values. This demonstrates that a single impact event (i.e. falls) cannot adequately describe all impact events. As a result, impact protocols which assess multiple impact events such as the protocol described in this study should be used to evaluate ice hockey goaltender masks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".