Decision management in transoral robotic surgery: Indications, individual patient selection, and role in the multidisciplinary treatment for head and neck cancer from a European perspective
Bibliographic record
Abstract
BACKGROUND: Transoral robotic surgery (TORS) has become an accepted first-line treatment for T1 and T2 head and neck squamous cell carcinoma (HNSCC). The growing popularity of this procedure is the result of mounting skepticism as to the survival and quality of life (QOL) benefits of primary chemoradiation over definitive surgery, the rising incidence of human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) in progressively younger patients, and the advantages of TORS over transoral laser microsurgery (TOLM) and open surgery. METHODS: The authors use their experience and data gained from the TORS-based management of >100 patients to establish a systematic approach to the use of TORS in HNSCC. RESULTS: This approach is constructed on a framework which goal is to select the primary treatment option that is most likely to reduce morbidity while preserving function and maintaining oncologic safety. CONCLUSION: A consensus regarding the indications of TORS and its role in the multidisciplinary management of HNSCC is to be established. © 2015 Wiley Periodicals, Inc. Head Neck 38: E2190-E2196, 2016.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".