12.8 Proposed injury thresholds for concussion in equestrian sports
Bibliographic record
Abstract
Objective Equestrian helmets are currently designed to pass certification standards based on linear drop tests onto rigid steel surfaces. However, concussion in equestrian sports occurs most commonly when a rider is thrown off a horse and obliquely impacts a compliant surface such as turf or sand. The objective of this work is to elucidate the mechanics of real-world impacts and thus propose clinically relevant thresholds for the occurrence of concussion. These should improve equestrian helmet standards and helmet designs. Design We used forensic reconstruction methods to examine the biomechanics of 25 concussive and 25 non-concussive well-documented real-world equestrian fall accidents, and thus to establish thresholds for the occurrence of concussive injury. Setting Video analysis, computational mechanics and physical simulation methods were combined to reconstruct all 50 real-world head impact accidents (Figure 1). Participants Male and female equestrian riders who sustained a fall and head impact during competitive racing and eventing. Outcome Measures Peak linear and angular accelerations, peak rotational velocity, maximum principal strain and maximum Von Mises stress sustained by riders during head-ground impact were quantified. Main Results Thresholds for a 50% risk of concussion were found to be 59g (linear acceleration, 2700rad/s2 (angular acceleration), 28rad/s (rotational velocity), 24% (maximum principal strain) and 6.6kPa (Von Mises stress). Conclusions Concussive equestrian accidents occurred from oblique impacts to turf or sand with lower magnitude and longer duration impacts (<130 g and >20 ms). This suggests that current equestrian helmet standards may not adequately represent real-world concussive impact conditions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".