Oncological and functional outcomes following transoral laser microsurgery in patients with T2a vs T2b glottic squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: There is a paucity of evidence comparing oncological and voice outcomes between T2a and T2b glottic squamous cell carcinoma (SCC) patients treated with transoral laser microsurgery (TLM). This study identified functional and oncological outcomes in this cohort. METHODS: Retrospective review of prospectively collected data of patients treated with TLM for T2 glottic SCC from 2003 to 2017. RESULTS: In total, 75 patients were included. Five-year local control rates were significantly different between T2a and T2b patients (75.2% vs 57.0%, p = 0.022). There was no difference in five-year survival between patients with T2a disease and T2b disease (69.5% vs 73.4%, p = 0.627). There was no significant difference in mean VHI-10 scores in the pre-operative period (18.3 vs 21.4, p = 0.409). However, patients with T2b disease had significantly worse perceived voice outcomes post-operatively (6.6 vs 21.3 p = 0.001). Patients with T2a disease saw significant improvements in mean VHI-10 scores following surgery (18.3 vs 6.6, p = 0.000), while T2b patients did not (21.4 vs 21.3, p = 0.979). The overall laryngeal preservation rate was 94.7%, with 11.5% of T2b patients requiring salvage organ sacrifice. CONCLUSIONS: This study highlights positive functional outcomes for T2a glottic SCC. Patients with T2b disease appear to have significantly worse oncological and functional outcomes, including worse voice quality following surgery and higher rates of salvage laryngectomy.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".