Surgical Margins and Handling of Soft-Tissue Sarcoma in Extremities: A Clinical Practice Guideline
Bibliographic record
Abstract
QUESTIONS: In limb salvage surgery for extremity soft-tissue sarcoma (sts), what is an adequate surgical margin?What is the appropriate number of samples to take from the margins of a surgical resection specimen?What is the appropriate handling of surgical resection specimens? BACKGROUND: Surgery is the primary treatment for extremity sts. The combination of radiotherapy with surgery allows for limb salvage by using radiation to biologically "sterilize" microscopic extensions of tumour and to spare neurovascular and osseous structures. Adjuvant chemotherapy in sts-except for rhabdomyosarcoma and Ewing sarcoma-continues to be controversial. METHODS: The medline and embase databases (1975 to June 2011) and the Cochrane Library were searched for pertinent studies. The Web sites of the main guideline organizations and the American Society of Clinical Oncology conference proceedings (2007-2010) were also searched. RESULTS AND CONCLUSIONS: Thirty-three papers, including four guidelines, one protocol, and one abstract, were eligible for inclusion. The data suggest that patients with clear margins have a better prognosis, but no prospective studies have indicated how wide margins should be. In limb-salvage surgery for extremity sts, the procedure should be planned to achieve a clear margin. However, to preserve functionality, surgery may result in a very close (<1 cm) or even microscopically positive margin. In this circumstance, the use of preoperative or postoperative radiation should be considered. No studies described the optimal number of tissue sections required to assess adequacy of excision nor the appropriate handling of surgical resection specimens. The Sarcoma Disease Site Group made its recommendations based on expert opinion and consensus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".