Curative-intent surgery for solitary bone metastasis from extremity and trunk wall sarcoma: What are the outcomes and complications?
Bibliographic record
Abstract
INTRODUCTION: Approximately 40-50 % of sarcoma patients will develop lung metastasis, but only 10 % will develop bone metastasis. The survival benefit of surgery for solitary bone metastasis remains unclear. METHODS: From 1987 to 2019, 47 patients who underwent curative-intent treatment for localized bone or soft tissue sarcoma in the extremities or trunk wall developed solitary bone metastases as the first distant recurrence. Of them, 51 % (24/47) received curative-intent metastasectomy. We compared the clinicopathologic characteristics of the metastasectomy versus non-metastasectomy patients and evaluated the prognostic impact of solitary bone metastasectomy. The primary outcome measure was disease-specific survival (DSS) after developing solitary bone metastasis. RESULTS: The post-metastasis DSS was worse with larger primary tumour size (HR 1.09; 95 % CI 1.02-1.16; p = 0.01) and bone metastasis in the pelvis or spine versus other bones (HR 3.79, 95 % CI 1.46-9.87; p = 0.01), and better with curative-intent surgery for the solitary bone metastasis (HR 0.14; 95 % CI 0.06-0.34; p < 0.001). The median DSS was 43 (95 % CI, 24-69) months for the metastasectomy group vs. 13 (95 % CI, 7-19) months for the non-metastasectomy group (p < 0.001). The metastasectomy group had fewer patients with metastasis in the spine or pelvis and longer metastasis-free interval. In the multivariate analysis, curative-intent surgery for solitary bone metastasis was associated with better survival (HR 0.21; 95 % CI 0.08-0.53; p = 0.001). CONCLUSIONS: Curative-intent surgery for solitary bone metastasis from sarcoma is associated with a better prognosis and is a reasonable treatment strategy whenever feasible.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".