A QCT-Based Nonsegmentation Finite Element Head Model for Studying Traumatic Brain Injury
Bibliographic record
Abstract
In the existing finite element head models (FEHMs) that are constructed from medical images, head tissues are usually segmented into a number of components according to the interior anatomical structure of the head. Each component is represented by a homogenous material model. There are a number of disadvantages in the segmentation-based finite element head models. Therefore, we developed a nonsegmentation finite element head model with pointwise-heterogeneous material properties and corroborated it by available experiment data. From the obtained results, it was found that although intracranial pressures predicted by the existing (piecewise-homogeneous) and the proposed (pointwise-heterogeneous) FEHM are very similar to each other, strain/stress levels in the head tissues are very different. The maximum peak strains/stresses predicted by the proposed FEHM are much higher than those by the existing FEHM, indicating that piecewise-homogeneous FEHM may have underestimated the stress/strain level induced by impact and thus may be inaccurate in predicting traumatic brain injuries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".