The Axial Impact Response and Plantar Load Distribution of the Hybrid III and Military Lower Extremity Under Altered Ankle Postures
Bibliographic record
Abstract
Foot injuries as a result of automotive collisions are frequent and impactful. Anthropomorphic test devices (ATDs), used to assess injury risk during impact scenarios such as motor vehicle collisions, typically assess risk of foot/ankle injuries by analyzing data in tibia load cells. The peak axial force and the tibia index are metrics commonly used to evaluate risk of injury to the lower extremity but do not directly account for injury risk to the foot, or the risk of injury associated with out-of-position loading. Two ATDs, the Hybrid III lower leg and the Military Lower Extremity, were exposed to axial impacts at seven different ankle postures. An array of piezoresistive sensors located on the insole of a boot was employed during these tests to assess the load distribution variations among postures and between ATD models on the plantar surface of the foot. Both posture and ATD model affected the load distribution on the foot, highlighting the need for regional injury risk assessments in this vulnerable anatomical region. The increase in forefoot loading during plantarflexion was not reflected in the standard industry metrics of peak axial force or tibia index, suggesting that increased fracture risk to the forefoot would not be detected. The variations in load distribution between the models could also alter injury risk assessment in frontal collisions based on differences in attenuation. These data could be used for regional foot injury assessment and to inform the design of an improved ATD foot.
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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) | 0.002 | 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".