Transoral robotic surgery for the identification of unknown primary head and neck squamous cell carcinomas: Its effect on the wait and the weight
Bibliographic record
Abstract
BACKGROUND: Neck carcinoma of unknown primary (CUP) is a frequent scenario. Transoral robotic mucosectomies (TORM) of pharynx have increased rate of primary identification, but come with cost of treatment delay. METHODS: We reviewed patients who underwent CUP protocol from 2014 to 2020. Patients with cervical nodes carcinoma and failure to localize a primary source were classified as CUP. We determined primary identification rate and postoperative complications. RESULTS: We included 65 patients underwent TORM. Surgical approach consisted of lingual and/or palatine tonsillectomies. The primary detection rate was 49.2%. Average weight reduction was 2.5 ± 4.3 kg. The average number of days from consultation to definitive treatment was 52.2 ± 18.3. CONCLUSION: A systematic approach to patients with CUP showed a promising primary identification rate compared to panendoscopy alone. TORM carries a small risk of complications. The benefits of primary identification must be weighed with the morbidity and delay to definitive treatment.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".