Free tissue transfer in reconstruction following soft tissue sarcoma resection
Bibliographic record
Abstract
BACKGROUND: Radical surgical resection remains the single-most important treatment in the curative multimodal therapy of soft tissue sarcomas. Refinements in surgical techniques have resulted in the development of function preserving approaches increasingly avoiding limb amputation. PATIENTS AND METHODS: The records of all patients (n = 34) who underwent microsurgical soft tissue coverage subsequent to primary resection of soft tissue sarcoma of the upper or lower limb from 1999 to 2009 are reviewed regarding postoperative complications, time until start of adjuvant radiation and functional outcome (Toronto Extremity Salvage Score, TESS). RESULTS: Thirty-four patients (range: 21-86 years) received a total of 35 free flaps. Complete tumor resection was obtained in 33 patients, one patient required re-excision ultimately resulting in tumor-free margin status (R0 resection). Major complications were encountered in four cases including one patient with complete flap loss requiring an additional free flap and three patients with partial flap loss requiring split-thickness skin graft procedures. Minor complications were observed in three patients (9%). Extremity salvage could be achieved in 33 patients with adequate postoperative ambulation (TESS 84 ± 18) and adequate use of the upper extremity (TESS 80 ± 22). One patient underwent amputation. Mean time until start of adjuvant radiotherapy was 37 days (range 24-56 days). CONCLUSION: A synergetic center-based interdisciplinary approach is crucial in therapeutical management of soft tissue sarcomas with the aim of R0 resection status and limb preservation. Plastic surgery contributes by offering microsurgical reconstruction using free tissue transfer, thus broadening surgical possibilities. This increases the chance of both adequate oncosurgical resection and limb preservation.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".