Transoral robotic surgery with radial forearm free flap reconstruction: Case control analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it