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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it