Collision Pressure and Dissipated Power Dose in a Self-Oscillating Silicone Vocal Fold Model With a Posterior Glottal Opening
Bibliographic record
Abstract
PURPOSE: The goal of this study was to experimentally evaluate how compensating for the adverse acoustic effects of a posterior glottal opening (PGO) by increasing subglottal pressure and changing supraglottal compression, as have been associated with vocal hyperfunction, influences the risk of vocal fold (VF) trauma. METHOD: A self-oscillating synthetic silicone model of the VFs with an airflow bypass that modeled a PGO was investigated in a hemilaryngeal flow facility. The influence of compensatory mechanisms on collision pressure and dissipated collision power was investigated for different PGO areas and supraglottal compression. Compensatory behaviors were mimicked by increasing the subglottal pressure to achieve a target sound pressure level (SPL). RESULTS: PGO area produced a 12% increase in the peak collision pressure, the dissipated collision power increased by 122%, mainly due to an increase in the magnitude of the collision velocity. This suggests that the value of peak collision pressure may not fully capture the mechanisms by which phonotrauma occurs. It was also found that an optimal value of supraglottal compression exists that maximizes the radiated SPL, indicating the potential utility of supraglottal compression as a compensatory mechanism. CONCLUSIONS: Larger PGO areas are expected to increase the risk of phonotrauma due to the concomitant increase in dissipated collision power associated with maintaining SPL. Furthermore, the risk of VF damage may not be fully characterized by only the peak collision pressure.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".