The influence of impact surface on head kinematics and brain tissue response during impacts with equestrian helmets
Bibliographic record
Abstract
Current equestrian standards employ a drop test to a rigid steel anvil. However, falls in equestrian sports often result in impacts with soft ground. The purpose of this study was to compare head kinematics and brain tissue response associated with surfaces impacted during equestrian accidents and corresponding helmet certification tests. A helmeted Hybrid III headform was dropped freely onto three different anvils (steel, turf and sand) at three impact locations. Peak linear acceleration, rotational acceleration and impact duration of the headform were measured. Resulting accelerations served as input into a three-dimensional finite element head model, which calculated Maximum principal strain (MPS) and von Mises stress (VMS) in the cerebrum. The results indicated that impacts to a steel anvil produced peak head kinematics and brain tissue responses that were two to three times greater than impacts against both turf and sand. Steel impacts were less than half the duration of turf and sand impacts. The observed response magnitudes obtained in this study suggest that equestrian helmet design should be improved, not only for impacts to rigid surfaces but also to compliant surfaces as response magnitudes for impacts to soft surfaces were still within the reported range for concussion in the literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".