Radiotherapy Versus Transoral Robotic Surgery for Oropharyngeal Squamous Cell Carcinoma: Final Results of the ORATOR Randomized Trial
Bibliographic record
Abstract
Radiotherapy (RT) and transoral robotic surgery (TORS) are both curative-intent treatment options for oropharyngeal squamous cell carcinoma (OPSCC). Herein, we report the final outcomes of the ORATOR trial comparing these modalities, 5 years after enrollment completion. We randomly assigned 68 patients with T1-2N0-2 OPSCC to RT (with chemotherapy if node-positive) versus TORS plus neck dissection (± adjuvant RT/chemoradiation). The primary end point was swallowing quality of life (QOL) assessed with the MD Anderson Dysphagia Inventory (MDADI). Secondary end points included overall and progression-free survival (OS, PFS), adverse events (AEs), and other QOL metrics. The primary end point has been previously reported (Nichols 2019). In this report, the median follow-up was 5.1 years (IQR, 5.0-5.3 years). MDADI total scores converged by 5 years and were not significantly different across the follow-up period ( P = .11). EORTC QLQ-C30 and H&N35 scores demonstrated differing profiles, including worse dry mouth in the RT arm ( P = .032) and worse pain in the TORS arm ( P = .002). Grade 2-5 AE rates did not differ between arms (91% [n = 31] v 97% [n = 33] respectively, P = .61), with more neutropenia and hearing loss in the RT arm, and more dysphagia and other pain in the TORS arm based on grades 2-5 (all P < .05). There were no differences in OS or PFS. In conclusion, toxicity and QOL profiles differ in some domains between RT and TORS, but oncologic outcomes were excellent in both arms. Choice of treatment should remain a shared decision between the patient and their providers.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".