Diagnosis and Management of Squamous Cell Carcinoma of Unknown Primary in the Head and Neck: ASCO Guideline
Bibliographic record
Abstract
PURPOSE: To provide evidence-based recommendations to practicing physicians and other health care providers on the diagnosis and management of squamous cell carcinoma of unknown primary in the head and neck (SCCUP). METHODS: The American Society of Clinical Oncology convened an Expert Panel of medical oncology, surgery, radiation oncology, radiology, pathology, and advocacy experts to conduct a literature search, which included systematic reviews, meta-analyses, randomized controlled trials, and prospective and retrospective comparative observational studies published from 2008 through 2019. Outcomes of interest included survival, local and regional disease control, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations. RESULTS: The literature search identified 100 relevant studies to inform the evidence base for this guideline. Four main clinical questions were addressed, which included subquestions on preoperative evaluations, surgical diagnostic and therapeutic procedures, appropriate pathology techniques, and adjuvant therapy. RECOMMENDATIONS: Evidence-based recommendations were developed to address preoperative evaluation for patients with a neck mass, surgical diagnostic and therapeutic procedures, appropriate treatment options in unilateral versus bilateral SCCUP.Additional information is available at www.asco.org/head-neck-cancer-guidelines.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".