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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".