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Record W4236363815 · doi:10.1121/1.4801044

Non-invasive in vivo measurement of the mechanical properties of human vocal fold tissue

2013· article· en· W4236363815 on OpenAlexaff
Siavash Kazemirad, Hani Bakhshaee, Luc Mongeau, Karen Kost

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsPhonationMagnetic resonance elastographyVocal foldsWave speedAcousticsWave propagationFold (higher-order function)Biomedical engineeringMaterials scienceAcoustic radiation forceIn vivoVibrationOpticsPhysicsAnatomyElastographyComputer scienceUltrasoundBiologyEngineeringLarynxMedicine

Abstract

fetched live from OpenAlex

A non-invasive method was developed and examined to obtain the mechanical properties of human vocal fold tissue in vivo via measurements of the mucosal wave propagation speed during phonation. Images of four human subjects' vocal folds were captured from the high speed imaging (HSI) and magnetic resonance imaging (MRI) experiments at different phonation pitches, the frequency of the vocal folds vibrations, in the range from 110 to 440 Hz. The MRI images were used to obtain the dimensions of the subjects' vocal folds in the high-speed images. The mucosal wave propagation speed was determined for each subject at different pitches using an automatic image processing algorithm. The shear modulus of the vocal fold mucosa in the transverse direction was then calculated from a surface (Rayleigh) wave propagation dispersion equation using the measured wave speeds. It was shown that the mucosal wave propagation speed and the shear modulus of the vocal fold tissue were generally greater at higher pitches. The results were in good agreement with those from other studies obtained via in vitro measurements, thereby supporting the validity of the proposed measurement method. This method offers the potential for in vivo clinical assessments.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.024
GPT teacher head0.244
Teacher spread0.220 · 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 designBench or experimental
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
Published2013
Admission routes1
Has abstractyes

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