Creating a human head finite element model using a multi-block approach for predicting skull response and brain pressure
Bibliographic record
Abstract
To better understand head injuries, human head finite element (FE) models have been reported in the literature. In scenarios where the head is directly impacted and measurements of head accelerations are not available, a high-quality skull model, as well as a high-quality brain model, is needed to predict the effect of impact on the brain through the skull. Furthermore, predicting cranial bone fractures requires comprehensively validated skull models. Lastly, high-quality meshes for both the skull and brain are needed for accurate strain/stress predictions across the entire head. Hence, we adopted a multi-block approach to develop hexahedral meshes for the brain, skull, and scalp simultaneously, a first approach in its kind. We then validated our model against experimental data of brain pressures (Nahum et al., 1977 Nahum AM, Smith R, Ward CC. 1977. Intracranial pressure dynamics during head impact. Proceedings of the 21st Stapp Car Crash Conference, SAE Paper No. 770922; Warrendale, PA: Society of Automotive Engineers.[Crossref] , [Google Scholar]) and comprehensive skull responses (Yoganandan et al., 1995 Yoganandan N, Pintar FA, Sances A, Jr., Walsh PR, Ewing CL, Thomas DJ, Snyder RG. 1995. Biomechanics of skull fracture. J Neurotrauma. 12(4):659–668.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar], Yoganandan et al., 2004 Yoganandan N, Zhang J, Pintar FA. 2004. Force and acceleration corridors from lateral head impact. Traffic Injury Prevention. 5(4):368–373.[Taylor & Francis Online] , [Google Scholar], and Raymond et al., 2009 Raymond D, Van Ee C, Crawford G, Bir C. 2009. Tolerance of the skull to blunt ballistic temporo-parietal impact. J Biomech. 42(15):2479–2485.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). We concluded that a human head FE model was developed with capabilities to predict blunt- and ballistic-impact-induced skull fractures and pressure-related 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".