Distribution of Brain Strain in the Cerebrum for Laboratory Impacts to Ice Hockey Goaltender Masks
Bibliographic record
Abstract
Concussions are among the most common injuries sustained by goaltenders. Concussive injuries are characterized by impairment to neurological function which can affect many different brain regions. Understanding how different impact loading conditions (event type and impact site) affect the brain tissue response may help identify what kind of impacts create a high risk of injury to specific brain regions. The purpose of this study was to examine the influence of different impact conditions on the distribution of brain strain for ice hockey goaltender impacts. An instrumented headform was fitted with an ice hockey goaltender mask and impacted under a protocol which was developed using video analysis of real world ice hockey goaltender concussions for three different impact events (collision, puck, and fall). The resulting kinematic response served as input into the University College Dublin Brain Trauma Model, which calculated maximum principal strain in the cerebrum. Strain subsets were then determined and analyzed. Resulting peak strains (0.124 - 0.328) were found to be within the range for concussion reported in the literature. The results demonstrated that falls and collisions produced larger strain subsets in the cerebrum than puck impacts which is likely a reflection of longer impact duration for falls and collisions than puck impacts. For each impact event, impact site was also found to produce strain subsets of varying size and configuration. The results of this study suggest that the location and number of brain regions which can be damaged depend on the loading conditions of the impact.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".