Imaging Soft-tissue Sarcomas of the Head and Neck: A Tertiary Soft-tissue Sarcoma Unit Experience
Bibliographic record
Abstract
BACKGROUND/AIM: To describe imaging features of head and neck soft-tissue sarcomas. PATIENTS AND METHODS: Patients with a diagnosis of head and neck sarcoma between 2011 and 2015 were reviewed. RESULTS: There were a total of 62 patients (24 female; median age=60 years). Most common sarcomas were angiosarcoma, undifferentiated pleomorphic sarcoma and sarcoma not otherwise specified. They were most commonly located in cranial and neck superficial soft tissues. Average tumour size at presentation was 45 mm. One patient had metastasis at presentation (rhabdomyosarcoma); two had nodal disease (rhabdomyosarcoma and angiosarcoma) and two tumours contained calcification (chondrosarcoma and synovial sarcoma). Four arose after prior radiotherapy. CONCLUSION: Unlike the more common diagnosis of squamous cell carcinoma, the majority of head and neck sarcomas present as large, solitary, superficial masses without lymph node enlargement. Identification of these features on imaging should raise suspicion of a sarcoma diagnosis, particularly in the setting of previous irradiation or genetic susceptibility.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".