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Temporalis muscle innervation patterns are generally conserved across subjects

2011· article· en· W3173257744 on OpenAlexaff
Jonathan J. Wisco, David Cantelmi, Joel Davies, Jayc C. Sedlmayr, Anne Agur

Bibliographic record

VenueThe FASEB Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicFacial Nerve Paralysis Treatment and Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAnatomyCadaveric spasmMicrodissectionMedicineBiology

Abstract

fetched live from OpenAlex

The temporalis muscle is structurally complex; its muscular architecture is often misrepresented and its innervation pattern is not well characterized. In this study we digitized and mapped the 3D extramuscular and intramuscular mandibular nerve course from foramen ovale to its terminal branches in the temporalis muscle belly in four formalin embalmed cadaveric specimens. The muscle mass was dissected and digitized in layers to map its volume. The digitized nerve and muscle volume data were reconstructed into a 3D model using Maya ® for visualization. Lateral 2D views were then manually registered to a common geometrical template using linear and non‐linear transformation techniques in Adobe Photoshop CS5. The extramuscular innervation consisted of three deep temporal nerves corresponding to anterior, middle and posterior portions of the muscle, masseteric and buccal nerves. Intramuscularly, the extent of muscle mass innervated by each of the nerves differed depending on the number of terminal branches. The spatial distribution of registered nerve branches was generally conserved suggesting that the degree of terminal branching was indicative of neurogenic control. We noted abundant intramuscular nerve fiber anastomosis from the anterior to posterior extent of the muscle in individuals. This work was supported by the American Association of Anatomists Visiting Scholarship.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.107
GPT teacher head0.324
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2011
Admission routes1
Has abstractyes

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