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Record W2091207375 · doi:10.1155/2014/738971

The Effect of Bolus Volume on Hyoid Kinematics in Healthy Swallowing

2014· article· en· W2091207375 on OpenAlexafffund
Ahmed Nagy, Sonja M. Molfenter, Melanie Péladeau-Pigeon, Shauna Stokely, Catriona M. Steele

Bibliographic record

VenueBioMed Research International · 2014
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of TorontoToronto Rehabilitation InstituteUniversity Health Network
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of HealthToronto Rehabilitation Institute
KeywordsSwallowingHyoid boneMedicineKinematicsAnatomyAirwayVestibuleBolus (digestion)EpiglottisLarynxSurgeryPhysicsAudiologyVestibular system

Abstract

fetched live from OpenAlex

Hyoid movement in swallowing is biomechanically linked to closure of the laryngeal vestibule for airway protection and to opening of the upper esophageal sphincter. Studies suggest that the range of hyoid movement is highly variable in the healthy population. However, other aspects of hyoid movement such as velocity remain relatively unexplored. In this study, we analyze data from a sample of 20 healthy young participants (10 male) to determine whether hyoid movement distance, duration, velocity, and peak velocity vary systematically with increases in thin liquid bolus volume from 5 to 20 mL. The temporal correspondence between peak hyoid velocity and laryngeal vestibule closure was also examined. The results show that maximum hyoid position and peak velocity increase significantly for 20 mL bolus volumes compared to smaller volumes, and that the timing of peak velocity is closely linked to achieving laryngeal vestibule closure. This suggests that generating hyoid movements with increased power is a strategy for handling larger volumes.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.524
Teacher spread0.449 · 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 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

Citations64
Published2014
Admission routes2
Has abstractyes

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