Outcomes of transoral laser microsurgical management of T<sub>1b</sub>stage glottic cancer
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the oncological and voice outcomes of transoral laser microsurgery for tumour stage T1b stage glottic cancer patients. METHODS: A prospective cohort study in a tertiary care head and neck cancer centre included tumour-node-metastasis stage T1bN0M0 glottic cancer patients scheduled to undergo transoral laser microsurgery from January 2002 until June 2014. Kaplan-Meier five-year analyses of local control, overall survival, disease-specific survival and laryngeal preservation were performed. Voice Handicap Index-10 scores and maximum phonation times were also recorded. RESULTS: Twenty-one participants with a mean age of 66.8 years were enrolled. The mean follow up was 56.5 months. Kaplan-Meier 5-year survival analysis illustrated a local control rate of 82 per cent, overall survival of 88 per cent, disease-specific survival of 100 per cent, and laryngeal preservation of 100 per cent. The pre-operative Voice Handicap Index-10 score was 19.1 ± 9.47 (mean ± standard deviation (SD)) and the post-operative scores were 13.5 ± 9.29 at three months, 10.44 ± 9.70 at one year and 5.83 ± 4.91 at two years. The pre-operative maximum phonation time was 16.23 ± 5.46 seconds (mean ± SD) and the post-operative values were 14.44 ± 6.73 seconds at three months, 15.27 ± 5.71 seconds at one year and 14.33 ± 6.44 seconds at two years. CONCLUSION: Transoral laser microsurgery yields relatively high rates of oncological control and acceptable voice outcomes, and thus shows utility as a primary treatment modality for T1b glottic cancer.
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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 | 0.001 |
| 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) | 0.002 | 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".