Investigation of tibia and fibula fracture risk during football impacts using finite element human body models
Bibliographic record
Abstract
Injuries in football are prevalent, and while shin guards reduce these, current test standards are primarily intended to evaluate contusion risk rather than more serious outcomes such as fractures. In this study, a finite element human body model was used to assess fracture risk in the lower leg subjected to conditions representative of football impacts. Various impactor shapes, impact locations and orientations were explored to identify conditions where fractures may be more likely to occur (based on element strain) and the associated force and bending moment. The lower leg was most susceptible to fractures at the 35% tibial height. Fractures occurred most frequently from the anterolateral direction, resulting in fibula injuries. In terms of geometry, the stud impactors were the most effective at inducing fractures and fracture was highly sensitive to bone alignment. Force to fracture ranged from 1595 to 2612 N. Susceptibility to fractures was influenced by the cross-sectional area of the bone, as well as the soft tissue thickness, with increased force attenuation associated with greater tissue thicknesses. This study showed various parameters affect the fracture tolerance of the lower leg, and identified the impact energies required to induce fractures, to better inform test standards for protective equipment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".