Soft tissue sarcoma presenting with metastatic disease
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess patient, tumor, and treatment factors that affected overall survival in a group of patients who underwent surgery for soft tissue sarcoma (STS) and presented with American Joint Commission on Cancer stage IV disease. METHODS: A retrospective review was undertaken of a single institution's database from the years 1986 to 2006 in all patients who met the following inclusion criteria: 1) surgical management of the primary tumor was undertaken, and 2) metastatic disease was present at the time of initial presentation. In total, 112 patients were identified who met the inclusion criteria. RESULTS: The 5-year survival rate for the entire group was 17%. In univariate analysis, the variables that were identified as statistically significant for predicting improved overall survival were resection of metastatic disease (P = .003), <4 pulmonary metastases (P = .05), and the presence of lymph node metastases versus pulmonary metastases (P = .0002). In multivariate analysis, only the presence of lymph node metastases versus pulmonary metastases retained statistical significance (P = .05). The 5-year survival rate for patients who had lymph nodes metastases at diagnosis was 59%, whereas it was only 8% for patients who presented with pulmonary metastases. CONCLUSIONS: Patients who presented with metastatic STS had a very poor prognosis despite aggressive surgical management of their primary tumor. The current results indicated that, although patients with isolated lymph node metastases may be cured by surgical resection, patients with pulmonary metastases are unlikely to be cured even with aggressive surgical management and should be treated with palliation of symptoms as the main objective.
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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".