Impact of Nasal Surgery on Speech Resonance
Bibliographic record
Abstract
OBJECTIVES: The nose and paranasal sinuses contribute to speech resonance and changes to these structures may alter speech nasality. This change may influence one's vocational and social functioning and quality of life. Our investigation explored objective and subjective changes in nasality following nasal surgery in a prospective and longitudinal fashion. METHODS: Recordings of sustained vowel and sentence stimuli and voice-related quality of life measurements were obtained preoperatively and at 2, 4, 8, and 24 weeks postoperatively from individuals undergoing nasal and/or sinus surgery. Objective measures of fundamental frequency, jitter, shimmer, and harmonic to noise ratio (HNR) were determined. Pre- and postoperative speech samples were assessed by 15 naïve listeners. RESULTS: In all, 15 subjects completed the study. Neither speakers nor listeners perceived a subjective change in nasality following surgery. No statistically significant change in microacoustic measures were identified. Although nasal sentences did not reveal differences for 3 microacoustic measures, a difference in HNR was identified. CONCLUSIONS: Patients undergoing nasal surgery did not exhibit subjective changes in resonance postoperatively. Aside from a difference in HNR for the nasal sentence, objective microacoustics remained unchanged. These results demonstrate the stability of oranasal resonance despite nasal surgery and provide valuable data for patient informed decision-making.
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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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".