Forearm soft tissue sarcoma: Tumors characteristics and oncologic outcomes following limb salvage surgery
Bibliographic record
Abstract
BACKGROUND: Complex anatomy of the forearm may impact on local control and survivals of soft tissue sarcoma. Little is known about characteristics and oncologic outcomes following surgical treatment. METHODS: Demographic and tumor data of 117 patients with forearm soft tissue sarcoma were collected and analyzed. Following limb salvage, survivals, and prognostic factors were studied. RESULTS: Seventy-three patients were males (62%) and 53 (45%) were referred after unplanned excision. Pleomorphic undifferentiated sarcoma was most frequent (45%). The average tumor size was 5.1 cm and grade III histology was mostly identified (53%). With radiotherapy, local recurrence occurs in 8 patients (7%) and 30 patients (24%) developed metastasis. Overall survival, disease free survival, local recurrence free survival, and metastasis free survival were 83%, 74%, 93%, and 74%, respectively. Better survival was found for grade I (80% vs. 60%) and small size (<5 cm) (72% vs. 47%). Large size tumor, extra-compartmental site, extramuscular, and virgin tumor were positive predictors of metastasis. CONCLUSION: Soft tissue sarcomas of the forearm are often referred after unplanned excision. Limb salvage was achieved for most and local recurrence remained low in context of radiotherapy. Metastatic progression remained frequent. Low grade and small size were predictors of survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".