Numerical Reconstruction of Traumatic Brain Injury in Skiing and Snowboarding
Bibliographic record
Abstract
PURPOSE: Proper evaluation of ski helmet designs and safety standards should rely on head impact conditions involved in skiing and snowboarding head injuries. To study these impacts, main crash scenarios involving head injuries are numerically replicated. METHODS: Multibody models of skiers and snowboarders were developed to investigate five common crash scenarios involved in traumatic brain injury: forward and sideways skiing falls, snowboarding backward falls, collisions between users and collisions with obstacles. For each scenario, the influence of crash conditions on head impact (location, speed, linear and rotational accelerations) and risk of injury are evaluated. Crash conditions were initial velocity, user height, position and approach angle, slope steepness, obstacles, and snow stiffness. RESULTS: One thousand one hundred forty-nine crashes were simulated and three significant levels of impact conditions were discriminated over the investigated crash scenarios: 1) the smallest normal-to-slope impact velocities (6 km·h; 22 km·h) and peak linear accelerations (42g; 75g) were obtained during forward and sideways skiing falls; 2) snowboarding backward falls and collisions between users were associated with high normal-to-surface impact velocities (26 km·h; 32 km·h) and head accelerations (80g; 149g) above one published threshold for mild traumatic brain injury but below the pass/fail criteria of helmet standard tests; 3) collisions with obstacles were associated with high normal-to-surface impact velocities (19 km·h; 35 km·h) and the highest head accelerations (626g; 1885g). CONCLUSIONS: Current impact conditions of helmet standard evaluations consistently replicate collisions with obstacles, but need to be revised to better reflect other significant crash scenarios leading to traumatic brain injury.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".