Transoral robotic surgery with radial forearm free flap reconstruction: Case control analysis
Bibliographic record
Abstract
BACKGROUND: The resection of large oropharyngeal tumors traditionally involves a lip-splitting mandibulotomy for adequate margin visualization and free flap reconstruction of the surgical defect. Transoral robotic surgery (TORS) has emerged as a technique that can resect large and complex oropharyngeal tumors, avoiding a lip-splitting approach. The aim of this study is to compare the lip-splitting mandibulotomy approach versus TORS for the management of advanced stage oropharyngeal carcinomas. METHODS: Prospectively collected data from 18 patients with advanced stage oropharyngeal squamous cell carcinoma (OPSCC) who received TORS with radial forearm free flap reconstruction (RFFF) was compared to a matched cohort of 39 patients who received a lip-splitting mandibulotomy and RFFF. Patients were matched for stage, p16 positivity, smoking, age and gender. Length of hospital stay (LOHS), tracheostomy decanulation time, operative time, surgical margin status, and post-operative complications were compared between groups. RESULTS: Patients who received TORS with RFFF had a significantly lower mean LOHS, compared to patients who were treated by lip-splitting mandibulotomy and RFFF (14.4 vs 19.7 days, p = 0.03). No significant differences were seen between groups in terms of operative time, tracheostomy decannulation time, margin positivity and post-operative complications. CONCLUSION: TORS with radial forearm free flap reconstruction is a safe, effective and cost-saving alternative to the lip-splitting mandibulotomy approach for the treatment of advanced stage OPSCC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".