Lymph Node Metastasis in Soft Tissue Sarcoma in an Extremity
Bibliographic record
Abstract
For patients with soft tissue sarcoma in an extremity, the outcome is thought to be poor if lymph node metastasis develops. The purpose of this study was to examine the impact of lymphatic involvement from soft tissue sarcoma on patient survival. Thirty-nine (3.7%) of 1066 patients who had surgery for soft tissue sarcoma in an extremity had lymph node metastases develop. Three (20%) of 15 patients with epithelioid sarcoma, four (19%) of 21 patients with rhabdomyosarcoma, two (11.1%) of 18 patients with clear cell sarcoma, and two (11.1%) of 18 patients with angiosarcoma had lymphatic involvement. Thirty patients who had resection of involved lymph nodes had an estimated 5-year survival of 57%, whereas nine patients treated without surgery all died within 30 months. An estimated 4-year survival of 71% for patients with isolated lymph node metastases was significantly better than 21% for patients with synchronous systemic and lymph node involvement. There was no difference in outcome for patients with isolated lymphatic involvement compared with patients with American Joint Committee on Cancer Stage III extremity sarcomas. These results suggest that long-term survival is possible after surgical resection of lymphatic metastases from soft tissue sarcoma. The American Joint Committee on Cancer should consider separating isolated nodal metastases from systemic involvement in patients with Stage IV sarcoma.
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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 | 0.000 | 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".