Comparison of Voice Characteristics Following Three Different Methods of Treatment for Laryngeal Cancer
Bibliographic record
Abstract
INTRODUCTION: Laryngeal cancer treatment has become more complex and diversified in past decades. Many different methods of treatment have evolved, and most have been able to restore the patient's function and maintain some form of functional speech. This study was designed to evaluate the voice and speech characteristics of patients who have undergone different treatments for laryngeal cancer and to compare those characteristics with those of age- and sex-matched normal laryngeal speakers. METHODS: Twenty-two male subjects participated in the study. Five men were treated with radiation therapy, 6 men had supracricoid partial laryngectomy, 6 men had undergone total laryngectomy with tracheoesophageal puncture, and 5 men were normal laryngeal speakers. Acoustic, aeromechanical, and perceptual assessments of speech were collected. RESULTS: Significant age effects were found for maximum phonation times. As age increased, maximum phonation time decreased (p < .005). Significant differences were found between groups for the following dependent variables: percentage of voiceless phonation, maximum phonation time, laryngeal airway resistance, subglottal pressure, oral flow, and word intelligibility. Trends in the data for differences between groups were noted for the following acoustic variables: noise-to-harmonics ratio, jitter, and shimmer. CONCLUSIONS: All patients developed or maintained a source of voicing after treatment and could use speech functionally, as demonstrated by normal sentence intelligibility. The radiation treatment group had voices that differed the least from the control group, whereas the opposite was true for the surgical groups, especially for those with total laryngectomy.
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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 | 0.002 |
| 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".