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Record W2758089427 · doi:10.1044/2017_jslhr-s-16-0277

Architecture of the Suprahyoid Muscles: A Volumetric Musculoaponeurotic Analysis

2017· article· en· W2758089427 on OpenAlexaff
Stephanie M. Shaw, Rosemary Martino, Ali Mahdi, Forrest Kip Sawyer, Sunita Mathur, Andrew Hope, Anne Agur

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Purpose: Suprahyoid muscles play a critical role in swallowing. The arrangement of the fiber bundles and aponeuroses has not been investigated volumetrically, even though muscle architecture is an important determinant of function. Thus, the purpose was to digitize, model in three dimensions, and quantify the architectural parameters of the suprahyoid muscles to determine and compare their relative functional capabilities. Method: Fiber bundles and aponeuroses from 11 formalin-embalmed specimens were serially dissected and digitized in situ. Data were reconstructed in three dimensions using Autodesk Maya. Architectural parameters were quantified, and data were compared using independent samples t-tests and analyses of variance. Results: Based on architecture and attachment sites, suprahyoid muscles were divided into 3 groups: anteromedial, superolateral, and superoposterior. Architectural parameters differed significantly (p < .05) across muscles and across the 3 groups, suggesting differential roles in hyoid movement during swallowing. When activated simultaneously, anteromedial and superoposterior muscle groups could work together to elevate the hyoid. Conclusions: The results suggest that the suprahyoid muscles can have individualized roles in hyoid excursion during swallowing. Muscle balance may be important for identifying and treating hyolaryngeal dysfunction in patients with dysphagia.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.108
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.111
GPT teacher head0.497
Teacher spread0.385 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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