Evaluating the Performance of a Hockey Helmet in Mitigating Concussion Risk Using Measures of Acceleration and Energy During Simulated Free Fall
Bibliographic record
Abstract
Hockey helmets represent the best form of head protection available to reduce the occurrence of skull fracture and concussion. Currently, helmet testing protocols focus on the reduction of peak linear acceleration measures. Gaps exist in analyzing how certain impact factors such as angle, neck stiffness, and location influence the energy loaded to the helmet and the risk of injury during head collisions. This study examined the effect of helmet impact angle, neck stiffness-torque levels, and helmet impact locations on energy reduction and risk of head injury grounded on acceleration measures using simulated free fall head collisions. The researchers conducted 540 impacts to collect the data. The results revealed statistical interaction effects between the angle of impact and location on measures of energy and risk of head injury. This study builds on existing literature by introducing an energy measurement technique to assess helmet performance. The outcome also provides an avenue for helmet manufacturers to evaluate the performance of the helmet in reducing concussion risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".