MétaCan
Menu
← Back to cohort
Record W7030124653

Modelling Physical Mechanisms of Nodule Development in Phonotraumatic Vocal Hyperfunction using Computational Vocal Fold Models

2023· dissertation· en· W7030124653 on OpenAlexfundno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicSubterranean biodiversity and taxonomy
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHyperfunctionVocal foldsPhonationStiffnessSpeech productionArticulatory phoneticsElectroglottograph
DOInot available

Abstract

fetched live from OpenAlex

Vocal hyperfunction is a prevalent voice disorder with significant impacts on the daily lives of patients, but has poorly understood causes. At its root, vocal hyperfunction is neurological, involving excessive muscular activation due to compensation for some underlying issue. In order to improve understanding of the causes of this disorder and ultimately improve its treatment, this thesis uses computational models to investigate mechanical aspects in the development of vocal fold nodules in phonotraumatic vocal hyperfunction (a specific class of vocal hyperfunction), specifically: whether biomechanical differences in stiffness of the vocal folds can lead to inefficient speech production that predisposes one to developing these nodule, and whether swelling can establish an amplifying feedback loop, a so-called "vicious cycle", wherein swelling leads to compensatory adjustments that incur further swelling and ultimately lead to nodule. To address these questions a two-dimensional finite-element vocal fold model coupled with a simplified one-dimensional flow model was developed with modifications to this basic model made to study the phenomena of interest. Towards modelling swelling, a computationally efficient approach to model the epithelium layer of the vocal folds is also developed and validated. \n \nTo investigate the first research question, the aforementioned model was adapted to study phonation onset pressure, a measure of effort required to produce speech, as a function of vocal fold stiffness. The results show that onset pressure is primarily dependent on just three stiffness distributions: smooth distributions with body-cover stiffness differences and smooth distributions with inferior-superior stiffness differences minimize onset pressure while a uniform stiffness increase increases onset pressure. Since a uniform stiffness increase increases the natural frequency of the vocal folds, this increase in onset pressure is roughly associated with increases in frequency. This suggests that for a given average stiffness (onset frequency) deviations from an optimal body-cover and inferior-superior-like distribution lead to increases in phonatory effort that could increase susceptibility to vocal hyperfunction. \n \nTo investigate the second research question, the finite element model was augmented with a model of swelling, as well as an epithelium using a membrane model. Results showed that swelling has negligible impact on loudness of speech but significantly influences frequency, and that furthermore, swelling increases measures of phonotrauma. These results suggest that swelling could incur a vicious cycle. Specifically, a decrease in fundamental frequency initiates compensatory adjustments through increased muscle tension and subglottal pressure, which tends to increase phonotrauma in the folds, and increased swelling with phonotrauma does not tend to limit further swelling. This result demonstrates how swelling can potentially lead to the formation of nodule.

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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.062
GPT teacher head0.194
Teacher spread0.132 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueUWSpace (University of Waterloo)→Same topicSubterranean biodiversity and taxonomy→French-language works237,207→