Explaining and predicting the increased thorax injury in aged females: age and subject-specific thorax geometry coupled with improved bone constitutive models and age-specific material properties evaluated in side impact conditions
Bibliographic record
Abstract
Predicting and understanding thorax injury is fundamental for the assessment and development of safety systems to mitigate injury risk to the increasing and vulnerable aged population. While computational human models have contributed to the understanding of injury biomechanics, contemporary human body models have struggled to predict rib fractures and explain the increased incidence of injury in the aged population. The present study enhanced young and aged human body models (HBMs) by integrating a biofidelic cortical bone constitutive model and population-based bone material properties. The HBMs were evaluated using side impact sled tests assessed using chest compression and number of rib fractures. The increase in thoracic kyphosis and the associated change in rib angle with increasing age, led to increased rib torsional moment increasing the rib shear stress. Coupled with and improved cortical bone constitutive model and aged material properties, the higher resulting shear stress led to an increased number of rib fractures in the aged model. The importance of shear stress resulting from torsional load was further investigated using an isolated rib model. In contrast, HBM chest compression, a common thorax injury-associated metric, was insensitive to the aging factors studied. This study proposes an explanation for the increased incidence of thorax injury with increasing age reported in epidemiological data, and provides an enhanced understanding of human rib mechanics that will benefit assessment and design of future safety systems.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".