Transoral robotic surgery vs open surgery in head and neck cancer. A systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: TORS has become one of the latest surgical alternatives in the treatment of oropharynx squamous cell carcinomas (OPSCC) and has become increasingly accepted by surgeons as a treatment option. Surgical robots were designed for various purposes, such as allowing remote telesurgery, and eliminating human factors like trembling. The study aimed to compare systematic review of the available literature in order to evaluate the safety and efficacy of Transoral Robotic Surgery (TORS) compared with open surgery. MATERIAL AND METHODS: We performed a systematic review of the available literature in order to evaluate the safety and effectiveness of TORS compared with open surgery. We compared TORS and open surgery based on 16 outcomes divided in to 3 groups: intra-operative complications, post-operative complications, and functional and oncologic outcomes. An electronic search of observational studies was carried out using the following databases: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Oral Health Group Trials Register, and Scielo. Data analysis was carried out in accordance to Preferred Reporting Items for Systematic Reviews and Metanalysis (PRISMA) and the quality of the studies were evaluated using the Newcastle-Ottawa Scale. No language restrictions were imposed. RESULTS: From the 4 studies identified (Newcastle-Ottawa Scale mean score 6.5), 371 patients were revised (186 patients were treated with TORS and 185 with conventional surgery). Overall, TORS, when compared with open surgery, appears to have better functional results (less hospital time, decannulation) and fewer intraoperative and post-operative complications. There is no significant difference when assessing the oncological outcomes (positive margins, survival rate) when comparing both techniques. CONCLUSIONS: TORS has an overall better functional outcome, and less intraoperative and postoperative complications with no difference in positive margins and survival rate when compared with conventional therapy.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".