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Record W4408548800 · doi:10.1080/10255842.2025.2472015

Is a 3D representation of muscle architecture needed to model craniomaxillofacial skeletal mechanics?

2025· article· en· W4408548800 on OpenAlexafffund
Hanieh Arjmand, Jeffrey A. Fialkov, Cari Whyne

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSunnybrook HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRepresentation (politics)ArchitectureComputer scienceSkeletal muscleMedicineGeographyAnatomyPolitical science

Abstract

fetched live from OpenAlex

The craniomaxillofacial skeleton (CMFS) is sensitive to the direction and magnitude of loading, making experimental testing challenging due to the complex network of musculature and thin bone. Finite element (FE) models have been used to characterize their mechanical behavior, often utilizing link elements to simulate muscles. Utilizing multimodal imaging, a specimen-specific CMFS FE model incorporating 3D masseter geometry and fiber directions was developed. 3D representation of the masseters resulted in lower peak intensity and smoother strain distribution in the zygomatic region, suggesting link muscle modeling may not sufficiently capture complex load transfer from muscle to bone in CMFS FE models.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.054
GPT teacher head0.413
Teacher spread0.359 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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