Transoral robotic surgery for head and neck malignancies: Imaging features in presurgical workup
Bibliographic record
Abstract
The objective of this article was to review the indications for transoral robotic surgery (TORS) in head and neck malignancies. The role of imaging in patient selection will be specifically reviewed. TORS is a recently developed technique that allows minimally invasive surgeries to be performed in the head and neck. TORS has a role in the de-escalation of oropharyngeal cancers, which allows for lower doses of chemoradiation therapy (this is a technique currently in clinical trials). Additionally, this technique allows for less invasive surgery and decreases associated complications. TORS can also be performed at other subsites. Cross-sectional imaging has a prominent role to help identify suitable candidates for this type of surgery. This article will review important anatomy and staging related to TORS. Additionally, the key imaging features for patient selection (indications and contraindications) will be presented along with case illustrations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".