The application of brain tissue deformation values in assessing the safety performance of ice hockey helmets
Bibliographic record
Abstract
This research was undertaken to examine a new method for assessing the performance of ice hockey helmets. It has been proposed that the current centric impact standards for ice hockey helmets, measuring peak linear acceleration, have effectively eliminated traumatic head injuries in the sport, but that angular acceleration and brain tissue deformation metrics are more sensitive to the conditions associated with concussive injuries, which continue to be a common injury. Ice hockey helmets were impacted using both centric and non-centric impact protocols at 7.5 m/s using a linear impactor. Dynamic impact responses and brain tissue deformations from the helmeted centric and non-centric head form impacts were assessed with respect to proposed concussive injury thresholds from the literature. The results of the helmet impacts showed that the method used was sensitive enough to distinguish differences in performance between helmet models. The results have shown that peak linear acceleration yielded low magnitudes of response to an impact, but peak angular acceleration and brain deformation metrics consistently reported higher magnitudes, reflecting a high risk for incurring a mild traumatic brain injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".