The role of transoral robotic surgery, transoral laser microsurgery, and lingual tonsillectomy in the identification of head and neck squamous cell carcinoma of unknown primary origin: A systematic review
Bibliographic record
Abstract
BACKGROUND: Squamous cell carcinoma of the head and neck can present as a cervical metastasis from an unknown primary site. Recently, transoral robotic surgery (TORS) and transoral laser microsurgery (TLM) have been incorporated in the workup of unknown primary tumors. METHODS: We searched MEDLINE, EMBASE, Cochrane, and CINAHL from inception to June 2015 for all English-language studies that utilized TORS, TLM, or lingual tonsillectomy in the approach to an unknown primary. RESULTS: Of 217 identified studies, eight were reviewed. TORS/TLM identified the primary tumor in 111/139 (80 %) patients overall, and 36/54 (67 %) patients with no remarkable findings following physical exam, radiologic imaging, and panendoscopy with directed biopsies. Lingual tonsillectomy identified the primary tumor in 18/25 (72 %) patients with no findings. Hemorrhage (5 %) was the most common perioperative complication. CONCLUSION: Lingual tonsillectomy using new approaches such as TORS/TLM may improve the identification of occult primary tumors.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.003 | 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".