The evaluation of speed skating helmet performance through peak linear and rotational accelerations
Bibliographic record
Abstract
OBJECTIVE: Like many sports involving high speeds and body contact, head injuries are a concern for short track speed skating athletes and coaches. While the mandatory use of helmets has managed to nearly eliminate catastrophic head injuries such as skull fractures and cerebral haemorrhages, they may not be as effective at reducing the risk of a concussion. The purpose of this study was to evaluate the performance characteristics of speed skating helmets with respect to managing peak linear and peak rotational acceleration, and to compare their performance against other types of helmets commonly worn within the speed skating sport. MATERIALS AND METHODS: Commercially available speed skating, bicycle and ice hockey helmets were evaluated using a three-impact condition test protocol at an impact velocity of 4 m/s. RESULTS AND DISCUSSION: Two speed skating helmet models yielded mean peak linear accelerations at a low-estimated probability range for sustaining a concussion for all three impact conditions. Conversely, the resulting mean peak rotational acceleration values were all found close to the high end of a probability range for sustaining a concussion. A similar tendency was observed for the bicycle and ice hockey helmets under the same impact conditions. CONCLUSION: Speed skating helmets may not be as effective at managing rotational acceleration and therefore may not successfully protect the user against risks associated with concussion injuries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".