Transoral Robotic Surgery for Recurrent Tumors of the Upper Aerodigestive Tract (RECUT): An International Cohort Study
Bibliographic record
Abstract
BACKGROUND: Transoral robotic surgery (TORS) is an emerging minimally invasive surgical treatment for residual, recurrent, and new primary head and neck cancers in previously irradiated fields, with limited evidence for its oncological effectiveness. METHODS: A retrospective observational cohort study of consecutive cases performed in 16 high-volume international centers before August 2018 was conducted (registered at clinicaltrials.gov [NCT04673929] as the RECUT study). Overall survival (OS), disease-free survival, disease-specific survivals (DSS), and local control (LC) were calculated using Kaplan-Meier estimates, with subgroups compared using log-rank tests and Cox proportional hazards modeling for multivariable analysis. Maximally selected rank statistics determined the cut point for closest surgical resection margin based on LC. RESULTS: Data for 278 eligible patients were analyzed, with median follow-up of 38.5 months. Two-year and 5-year outcomes were 69.0% and 62.2% for LC, 71.8% and 49.8% for OS, 47.2% and 35.7% for disease-free survival, and 78.7% and 59.1% for disease-specific survivals. The most discriminating margin cut point was 1.0 mm; the 2-year LC was 80.9% above and 54.2% below or equal to 1.0 mm. Increasing age, current smoking, primary tumor classification, and narrow surgical margins (≤1.0 mm) were statistically significantly associated with lower OS. Hemorrhage with return to theater was seen in 8.1% (n = 22 of 272), and 30-day mortality was 1.8% (n = 5 of 272). At 1 year, 10.8% (n = 21 of 195) used tracheostomies, 33.8% (n = 66 of 195) used gastrostomies, and 66.3% (n = 53 of 80) had maintained or improved normalcy of diet scores. CONCLUSIONS: Data from international centers show TORS to treat head and neck cancers in previously irradiated fields yields favorable outcomes for LC and survival. Where feasible, TORS should be considered the preferred surgical treatment in the salvage setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".