A parametric analysis of factors that determine head injury outcomes following equestrian fall accidents
Bibliographic record
Abstract
This study examined the effects and interaction of four primary impact parameters (impact velocity, angle of impact relative to the ground, ground compliance and helmet impact location) on head kinematics and brain tissue response for falls that are most commonly associated with equestrian sports. A helmeted headform was subject to parametric tests using a rail guided launcher at three impact velocities (6, 9 and 12 m/s), four angles of incidence (15°, 30°, 45° and 60°), three ground compliance levels (High, Medium and Low) and three helmet locations (front, front-boss and rear-boss). Head kinematics were obtained from the headform and a finite element model was used to estimate brain tissue response. Velocity and angle had the largest effects on the risk of concussion, as measured by head kinematics and brain tissue response, while compliance and location were less influential. Interactions such as angle and compliance were found to greatly influence the risk of concussion. These findings suggest that an increased ground compliance can decrease linear acceleration and Head Injury Criterion (HIC) but not necessarily decrease rotational kinematics and brain tissue response. Consequently, the use of technological designs to attenuate rotational acceleration and decouple the helmet from that of the head may provide better safety than simply the addition of extra protective layers to the ground or a helmet liners.
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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 | 0.007 |
| 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) | 0.001 | 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".