Effects of surface compliance on the dynamic response and strains sustained by a player’s helmeted head during ice hockey impacts
Bibliographic record
Abstract
In hockey, players experience different compliances during impacts to the head, from stiff ice to compliant collisions against other players. The objective of this study was to examine the effect of striking compliance in ice hockey impacts and its influence on dynamic response and brain tissue strain. Three striking caps of low, medium, and high compliances were used to impact a helmeted 50th-percentile Hybrid III headform. The headform was impacted at five locations at three velocities, representative of collision scenarios in hockey. The dependent variables, peak resultant linear and rotational acceleration as well as maximum principal strain were analyzed using a multivariate analysis of variance to determine significant differences between the compliances. The results indicated a significant effect of compliance on the responses of the headform. As expected, low-impact compliance resulted in higher linear and rotational accelerations when compared to the medium and high compliance conditions. However, while the linear and rotational acceleration responses of the medium and high compliance conditions would indicate a low chance of brain injury, the maximum principal strain magnitudes indicated a high likelihood of concussion. Medium- and high-impact compliances are a factor that has not been considered when designing and testing helmet technology in sport, with current methods reflective of low compliance surfaces, that is, those with high stiffness and rigidity. The results of this study demonstrate that an impact compliance is an important factor in producing brain injury and should be considered when certifying helmets through standard testing to mitigate the risk of brain injury.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".