Modeling the Pathophysiology of Phonotraumatic Vocal Hyperfunction With a Triangular Glottal Model of the Vocal Folds
Bibliographic record
Abstract
Purpose: Our goal was to test prevailing assumptions about the underlying biomechanical and aeroacoustic mechanisms associated with phonotraumatic lesions of the vocal folds using a numerical lumped-element model of voice production. Method: A numerical model with a triangular glottis, posterior glottal opening, and arytenoid posturing is proposed. Normal voice is altered by introducing various prephonatory configurations. Potential compensatory mechanisms (increased subglottal pressure, muscle activation, and supraglottal constriction) are adjusted to restore an acoustic target output through a control loop that mimics a simplified version of auditory feedback. Results: The degree of incomplete glottal closure in both the membranous and posterior portions of the folds consistently leads to a reduction in sound pressure level, fundamental frequency, harmonic richness, and harmonics-to-noise ratio. The compensatory mechanisms lead to significantly increased vocal-fold collision forces, maximum flow-declination rate, and amplitude of unsteady flow, without significantly altering the acoustic output. Conclusion: Modeling provided potentially important insights into the pathophysiology of phonotraumatic vocal hyperfunction by demonstrating that compensatory mechanisms can counteract deterioration in the voice acoustic signal due to incomplete glottal closure, but this also leads to high vocal-fold collision forces (reflected in aerodynamic measures), which significantly increases the risk of developing phonotrauma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".