Outcomes of surgical treatment alone in patients with superficial soft tissue sarcoma regardless of size or grade
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Currently, standard treatment of soft tissue sarcoma (STS) is wide local excision and adjuvant radiation, but radiation may be unnecessary in superficial STS. The primary objective is to assess local recurrence rates in patients treated with surgical management alone for superficial STS. METHODS: A retrospective cancer registry review of patients treated with surgery alone for superficial STS at the Tom Baker Cancer Center (TBCC) was performed. Patient and tumor characteristics as well as recurrence data were collected. RESULTS: Sixty-one patients met study criteria. Local and overall recurrence rates were 7/61 (11.5%) and 12/61 (19.7%), respectively. The proportion with a T2 tumor was 38.8% versus 33.3% (P = 0.69), with Grade 2 or 3 tumors was 59.2% versus 83.3% (P = 0.14), and with resection margins <1 cm was 28.6% versus 75.0% (P = 0.008) for patients without and with recurrence, respectively. Median time to recurrence was 1.7 (0.4-5.2) years. CONCLUSIONS: Surgical resection alone appears to be a viable option for superficial STS that can save patients from potential side effects of radiation. The association between recurrence and inadequate margins (<1 cm) requires additional treatment be offered to this subset of patients.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".