Evaluating the Efficacy of Transoral Robotic Surgery (TORS) Versus Radiotherapy, Chemotherapy, and Open Surgery in Treating Oropharyngeal Squamous Cell Carcinoma (OPSCC): A Systematic Review of Reviews
Bibliographic record
Abstract
Introduction: Transoral robotic surgery (TORS) is a minimally invasive surgical approach for oropharyngeal squamous cell carcinoma (OPSCC) that aims to reduce morbidity and improve patients' quality of life without compromising oncological outcomes. In this study, we investigate the use of TORS in the management of OPSCC and compare it with intensity-modulated radiation therapy (IMRT), concurrent chemoradiation therapy (CCRT), and open surgery. Method: We conducted a systematic review of systematic reviews using PubMed, Cochrane databases, and grey literature. We also searched the reference lists of these articles. The keywords used were "trans-oral robotic surgery" OR "TORS" AND "oropharynx" OR "oropharyngeal cancer". The inclusion criteria were systematic reviews of human studies that focused on patients diagnosed with OPSCC. We excluded non-English articles without translations and articles that did not meet the inclusion criteria. Result: Our review included a total of 10 studies, comprising 16,917 patients. TORS was found to have better oncological outcomes than other modalities, and was associated with similar overall survival and disease-free survival rates as IMRT and CCRT. Additionally, TORS was associated with less postoperative bleeding than open surgery. Conclusion: Our findings suggest that TORS is a safe and effective treatment option for OPSCC. It may be a good option for patients seeking a minimally invasive approach with less postoperative bleeding.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".