The Role of Transoral Robotic Surgery in the Management of Oropharyngeal Cancer: A Review of the Literature
Bibliographic record
Abstract
Background. Transoral robotic surgery (TORS) is an emerging treatment option for the treatment of head and neck malignancies, particularly for oropharyngeal squamous cell carcinoma (OPSCC). Preliminary studies have demonstrated excellent oncologic and functional outcomes that have led to a resurgence of interest in the primary surgical management of OPSCC. The aim of the present study was to review the evidence base supporting the use of TORS in OPSCC. Methods. Studies evaluating the application of TORS in the treatment of head and neck squamous cell carcinoma (HNSCC), and more specifically OPSCC, were identified for review. Further searches were made of reference lists for complete evaluation of minimally invasive surgery (MIS) in treating OPSCC. Results. Seventeen results relating to the application of TORS in treatment of OPSCC were identified. Further results relating to the role of transoral laser microsurgery (TLM) in OPSCC were included for review. Feasibility, oncologic, and functional data is summarized and discussed. Discussion. Management strategies for patients with OPSCC continue to evolve. Minimally invasive surgical techniques including TORS and TLM offer impressive functional and oncologic outcomes particularly for patients with early T-classification and low-volume regional metastatic disease. Potential exists for treatment deintensification, particularly in patients who are HPV positive.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".