Voice Outcomes Following Repeated Surgical Resection of Laryngeal Papillomata in Children
Bibliographic record
Abstract
OBJECTIVES: 1) To apply perceptual and acoustic voice assessments to children treated for juvenile-onset recurrent respiratory papillomatosis (JORRP); 2) to compare voice outcomes following treatment for JORRP using microdebrider versus carbon dioxide (CO(2)) laser. STUDY DESIGN: Prospective cohort study. SETTING: This study was conducted at a tertiary pediatric academic center (March 2008-March 2009). SUBJECTS AND METHODS: Children with active JORRP were assessed using perceptual and acoustic voice analysis following treatment with either CO(2) laser or microdebrider. Outcome measures included overall severity rating, jitter, shimmer, and noise-to-harmonic ratio (NHR). The unpaired Student t test and Pearson correlation tests were used to explore the statistical significance of hypothesis tests. RESULTS: Eleven patients (8 male, 3 female) aged three to 17 years were enrolled. There were six children in the CO(2) laser cohort and five children in the microdebrider cohort. The immediate postoperative scores were significantly lower in the microdebrider cohort (vs the CO(2) cohort) for jitter, shimmer, NHR, and perceptual scores (P < 0.05), indicating a better voice quality in the microdebrider group. Jitter, shimmer, and NHR showed a significant positive correlation with the proportion of CO(2) laser procedures (P < 0.05). CONCLUSION: This is the first study to use perceptual and objective acoustic evaluations to compare voice outcomes following microdebrider or CO(2) laser treatment of JORRP. The results of this study suggest that treatment with the microdebrider results in a better immediate and early postoperative voice outcome. Moreover, the data demonstrate a correlation of worsening voice quality with increased exposure to the CO(2) laser.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".