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Record W2905848362 · doi:10.1080/10255842.2018.1541983

Creating a human head finite element model using a multi-block approach for predicting skull response and brain pressure

2018· article· en· W2905848362 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2018
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsWestern University
FundersNational Key Research and Development Program of ChinaChina Postdoctoral Science FoundationCanada Research Chairs
KeywordsSkullHuman headPolygon meshHead (geology)Human skullComputer scienceFinite element methodGeologyEngineeringMedicineAnatomyStructural engineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.393
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it