Finding/identifying primaries with neck disease (FIND) clinical trial protocol: a study integrating transoral robotic surgery, histopathological localisation and tailored deintensification of radiotherapy for unknown primary and small oropharyngeal head and neck squamous cell carcinoma
Bibliographic record
Abstract
INTRODUCTION: Carcinomas of unknown primary site (CUP) of the head and neck have historically been worked up and managed heterogeneously. Failure to identify a primary site may result in large radiotherapy mucosal volumes. Transoral approaches such as Transoral Robotic Surgery (TORS) may improve the yield of identifying hidden primaries. We aim to assess the oncological and functional outcomes of a combined treatment approach with TORS and tailored radiotherapy. METHODS AND ANALYSIS: Twenty-five patients with metastatic squamous cell carcinoma to the neck without clinical or radiographic evidence of a primary site will be enrolled in a phase II trial. Patients will undergo a diagnostic or therapeutic approach with TORS based on specific algorithms incorporating tailored radiotherapy according to the location and laterality of the primary tumour. The primary outcome is to evaluate the out-of-field failure rate over a 2-year period. Secondary outcomes include identification rates, survival outcomes, patient reported outcomes and functional swallowing outcomes. ETHICS AND DISSEMINATION: The University Health Network Research Ethics Board approved this study (ID 15-9767). The results will be published in an open access journal. TRIAL REGISTRATION NUMBER: NCT03281499.
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.
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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".