Voice and Functional Outcomes of Transoral Laser Microsurgery for Early Glottic Cancer: Ventricular Fold Resection as a Surrogate
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to evaluate the oncological and functional outcomes with transoral laser microsurgery (TOLM) of patients with early glottic cancer. METHODS: We have prospectively evaluated patients treated with TOLM for Tis, T1 or T2 glottic squamous cell carcinoma. Evaluation of oncological outcomes, and voice and functional outcomes was assessed using voice-handicap index 10 (VHI-10) and performance status scale for head & neck cancer patients (PSS-H&N). Predictors of poor voice quality were evaluated using Student's t-test. RESULTS: Thirty patients were included, with 17.7 months mean follow-up. There were no cases of locoregional recurrence. Twelve patients (40%) were considered as having a problematic voice outcome. Four subjects out of 30 (13.3%) had significant problems with understandability of speech. Significant differences (P < 0.05) in VHI-10 score were found with tumor stage and partial resection of the ventricular fold. CONCLUSIONS: We report excellent oncological and functional outcomes in early glottic cancer treated with TOLM, with advanced tumors and partial resection of the ventricular fold as a surrogate predicting worse voice outcomes.
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 | 0.001 | 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".