Soft‐Tissue Sarcomas of the Head and Neck: A Retrospective Analysis of the Alberta Experience 1974 to 1999
Bibliographic record
Abstract
BACKGROUND: Soft-tissue sarcomas (STS) of the head and neck constitute a heterogeneous group of rare malignant tumors occurring in an uncommon site. The most common subtypes of STS in the head and neck are malignant fibrous histiocytoma, dermatofibrosarcoma protuberans, and fibrosarcoma. Evidence based subtype-specific treatment decisions are often not possible. METHODS: The medical records of 110 patients diagnosed with head and neck sarcomas were reviewed. All were treated at one of the two major Cancer Centers in Alberta, Canada, between 1974 and 1999. Potential prognostic factors including age, sex, tumor size, histology, grade, tumor location (superficial or deep), and use of adjuvant treatment were evaluated. Cox proportional hazards models were developed to study the impact of these covariates on survival. RESULTS: The median duration of follow-up was 61.5 months. Five year overall, disease specific, and relapse free survival were 65.8%, 83.4%, and 74.2%, respectively. With use of a Cox proportional-hazards model, tumor stage and grade were important prognostic factors affecting survival. CONCLUSIONS: Tumor size and grade were important prognostic factors affecting survival. Tumor location in relation to the superficial fascia (depth) was the best predictor of outcome. The overall and disease-free survival in this patient group was excellent. However, this likely caused by the high proportion of patients with low-grade tumors in our study.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".