Transoral tongue base mucosectomy for the identification of the primary site in the work-up of cancers of unknown origin: Systematic review and meta-analysis
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: The use of transoral robotic surgery (TORS) and laser microsurgery (TLM) in the diagnosis and identification of the site of the unknown primary has become increasingly common. This systematic review and meta-analysis aims to assess the use and efficacy of TORS and TLM for this indication. METHOD: Systematic review and meta-analysis of studies employing TORS or TLM in diagnosis of the unknown primary tumor site in patients with cervical nodal metastases of squamous cell origin. MEDLINE, EMBASE and CINHAL were searched from inception to July 2018 for all studies that used TORS and or TLM in identifying the unknown primary. RESULTS: 251 studies were identified, of which 21 were eligible for inclusion. The primary tumour was identified by TORS/TLM in 78% of patients (433 out of 556). Tongue base mucosectomy (TBM) identified the primary in 222 of 427 cases (53%). In patients with negative physical examination, diagnostic imaging and PETCT, TBM identified the primary in 64% (95% CI 50, 79) cases. In patients who had negative CT/MRI imaging, negative PETCT and negative EUA and tonsillectomy, TBM identified a tongue base primary in 78% (95% CI 41, 92) cases. Haemorrhage, the commonest complication, was reported in 4.9% cases. Mean length of stay varied between 1.4 and 6.3 days. CONCLUSION: Tongue base mucosectomy, performed by TORS or TLM, is highly efficacious in identifying the unknown primary in the head and neck region.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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