Effect of Load Rate on the Fracture Tolerance of the Tibia
Bibliographic record
Abstract
Fractures of the lower leg are common during frontal automotive collisions and military blasts. These two scenarios cause injury via a similar axial loading mechanism. The majority of previous studies that have conducted axial impact tests to determine the injury limits of the lower leg have simulated automotive impacts; however, due to the viscoelastic nature of bone, it remains unclear whether limits from automotive experiments can be applied to higher-rate blasts. The purpose of this work was to study the effect of load rate on the fracture tolerance of the tibia during these two scenarios. The instrumentation required to quantify impacts to lower leg specimens using a pneumatic impactor was developed, and included capturing synchronized load, acceleration, velocity, strain, and high-speed video data. Subsequently, impact testing was performed on twelve human cadaveric tibias. Velocities and impact durations were matched to literature values to simulate an automotive collision and a military blast. Force and impulse were found to significantly differ between the two conditions, while kinetic energy did not. Specimens impacted at higher rates required greater forces to achieve fracture, which suggests that load rate needs to be accounted for in future injury criteria. Two commonly used anthropomorphic test device lower legs were tested under similar loading conditions, and new thresholds were developed for these devices. Finally, a finite element model was tested for its ability to simulate loading of the tibia during varied impacts. This model can be used to assess injury risk and protective measures for the leg. Understanding the effect of load rate on the tibia’s fracture tolerance is essential when developing injury thresholds that can be applied to impacts of various rates. The results of this work can be used in the future to design and evaluate improved protective systems to be implemented in vehicles.
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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 | 0.003 |
| 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".