Head and Neck Injury Potential With and Without Helmets During Head-First Impacts on Snow
Bibliographic record
Abstract
Terrain parks and jumping features at ski resorts have become increasingly popular with skiers and snowboarders over the past decade. If a jumper were to land incorrectly, such as in an inverted posture where one lands on their head, the consequences can be devastating and can result in cervical spine fractures or dislocations and serious spinal cord injury. The objective of this study was to assess the potential for serious neck injury in head-first impacts onto snow surfaces with and without helmets. We conducted six paired head-first impact drop tests, with and without helmets on snow that varied from soft to hard. Drop tests were carried out with a head and neck assembly from a Hybrid III anthropomorphic test device using a custom designed drop carriage. The impact speed was 4.0 ± 0.1 m/s, representing an equivalent fall height of 0.82 m. The head was instrumented with three uniaxial accelerometers located at the center-of-gravity and a six-axis load cell was located at the upper neck. The results indicated that the helmets provided good head protection in the hard snow impacts, reducing head accelerations by as much as 48 %. Head accelerations were low in soft snow impacts both with and without a helmet. Overall, helmets were not an effective countermeasure to high neck loads, although a minor reduction was noted in the soft snow impacts. All tests resulted in neck loads that exceeded the injury assessment reference values for the neck. Notably in the hard snow impacts, the neck loads were more than double the injury assessment reference values for all tests. Because of the susceptibility of the neck to injury at the relatively low drop heights that we tested, efforts to prevent neck injuries should focus on education and training to avoid head-first impact.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".