Estimation of Injury Limits at Vulnerable Impact Locations Along the Forearm Via THUMS AM50 Finite Element Model at Airbag Loading Rates
Bibliographic record
Abstract
Side and frontal airbag deployment represents the main injury mechanism to the upper extremity during automotive collisions. Previous dynamic injury limit research has been limited to testing the forearm at either the assumed most vulnerable location to fracture, the distal 1/3rd, or the midpoint. Studies have varied the surface to which impacts were applied, with no clear consensus on the site of greatest vulnerability. The unpredictability of airbag impact location, especially with altered hand positioning, limits the effectiveness of existing forearm injury limits determined from impacts at only one location. The current study quantified the effect of impacts at alternative locations on injury risk along the forearm using the THUMS FE model. Airbag-level impacts were simulated along the forearm on all four anatomical surfaces. Results showed the distal 1/3rd is not the most vulnerable location (for any side), indicating forearm fracture is not solely driven by area moment of inertia (as previously assumed). The posterior forearm was the weakest, suggesting that current test standards underestimate the fracture risk of the forearm. Linear regression models showed strong correlation between forearm fracture risk and bone geometry (cross-sectional area and area moment of inertia) as well as soft-tissue depth, potentially providing the ability to predict forearm injury tolerances for any location or forearm size. This study demonstrated the forearm's vulnerability to fracture from airbag deployments, indicating the need for safety systems to better address injury mechanisms for the upper limb to effectively protect drivers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".