Isolated Rib Response and Fracture Prediction for Young Mid-Size Male, Enabled by Population Specific Material Models and Rib Cross-Sectional Geometry
Bibliographic record
Abstract
Thorax injury remains a primary contributor to mortality in car crash scenarios. Although human body models can be used to investigate thorax response to impact, isolated rib models have not been able to predict age- and sex-specific force-displacement response and fracture location simultaneously, which is a critical step towards developing human thorax models able to accurately predict injury response. Recent advancements in constitutive models and quantification of age- and sex-specific material properties, cross-sectional area, and cortical bone thickness distribution offer opportunities to improve rib computational models. In the present study, improved cortical and trabecular bone constitutive models populated with age-specific material properties, age- and sex-specific population data on rib cross-sectional area, and cortical bone thickness distribution were implemented into an isolated 6 th rib from a contemporary human body model. The enhanced rib model was simulated in anterior-posterior loading for comparison to experimental age- and sex-specific (twenty-three mid-size males, age range of 22- to 57-yearold) population force-displacement response and fracture location. The improved constitutive models, populated with age-specific material properties, proved critical to predict the rib failure force and displacement, while the improved cortical bone thickness distribution and cross-sectional area improved the fracture location prediction. The enhanced young mid-size male 6 th rib model was able to predict young mid-size male 6 th rib experimental force-displacement response and fracture location (overpredicted the displacement at failure by 35% and underpredicted the force at failure by 8% but within ±1 SD). The results of the present study can be integrated into full body models to potentially improve thorax injury prediction capabilities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".