A Two-Step Approach to the Surgical Treatment of Soft-Tissue Sarcomas
Bibliographic record
Abstract
Background: Nowadays, limb-sparing procedures are the gold standard in the treatment of soft-tissue sarcomas of the limbs. Wide tumor resection with appropriate oncological margins, reconstruction, and stabilization of the involved bone and joint and restoration of the soft tissue lost are essential in order to obtain good clinical and functional outcomes. Tumor excision and soft-tissue reconstruction performed in one-step surgery is chosen by many centers as the preferred approach; however, according to our experience in some selected patients, two-step surgery performed using a dermal regeneration template first and then a margin revision, taking into account the definitive results of the anatomopathological exam conducted over the surgical specimen from the previous surgery, associated with definitive reconstruction surgery over a healthy bed of granulated tissue, showed many potential benefits. Methods: A retrospective observational study was conducted on thirteen patients who underwent a two-step reconstruction procedure using dermal substitution after soft-tissue sarcoma excision. Results: Clinically, the enrolled patients achieved excellent contour and cosmesis of their surgical wounds, with a mean VSS value of 3.07. During the follow-up period, no local recurrences were observed in any patient. Conclusions: Two-step surgery represents the most suitable solution to allow surgical radicality with minimal recurrency and adequate soft-tissue reconstruction, avoiding the possibility of wasting autologous tissue. Our patients generally embraced this approach and the management that followed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".