A three-dimensional finite element model of a 6-year-old child for simulating brain response from physical reconstructions of head impacts
Bibliographic record
Abstract
Despite young children being a high-risk population for sustaining concussive injuries in sport, few studies have investigated head impact biomechanics from sporting impacts using physical models and finite element models of the brain. Physical reconstructions are often used in concussive research, using the recorded kinematics to load finite element models of the brain to obtain better information of real-life head injuries. For children, scaling adult models is a common method used to study the youth population. However, this method does not capture age-dependent material properties or the unique geometry of the developing brain. To address these deficiencies, a novel three-dimensional finite element model of a 6-year-old child was developed and compared to a scaled adult model, for use with physical reconstructions. With the lack of intracranial validation data for the youth population, adult cadaveric data for brain motion was used for comparison. The new brain model showed unique responses in motion and strain compared to the scaled adult model. Using the normalized integral square error method, the new model was classified to have ‘fair’ to ‘excellent’ biofidelity. The new model is proposed as more appropriate for conducting concussion and brain injury research in young children near 6 years of age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".