VAC temporization pending final margins after suprafascial myxofibrosarcoma excision to reduce the rate of local recurrence.
Bibliographic record
Abstract
11573 Background: The microinvasive nature of suprafascial myxofibrosarcoma complicates the accuracy of intraoperative margin assessment, and tumor bed resections after soft tissue reconstruction are unreliable. For the past 3 years we have temporized the excised tumor bed with a wound VAC, delaying soft tissue coverage until final negative margins were achieved. Here, we compare the oncologic/surgical outcomes of suprafascial myxofibrosarcomas managed with VAC temporization (VT) with single-stage excision/reconstruction (SS). Methods: We retrospectively studied suprafascial myxofibrosarcomas managed from 1/1/2000 to 1/1/2019 who received neoadjuvant or adjuvant radiation and had at least 2-years of oncologic follow-up at a tertiary referral cancer center. Our primary outcome was local recurrence. Comparisons were performed using Fisher’s Exact Test or Students t-test. A p-value < 0.05 was considered significant. Results: Fifty-three patients (18 VAC temporized, 35 single stage) were included. While VT patients were older (74.9 ± 10.2 vs. 63.9 ± 13.6, p = 0.003), treatment groups did not significantly differ with respect to comorbidity, tumor volume, stage and grade. VT patients had significantly fewer local recurrences (5.6% vs. 28.6% after SS, p = 0.048) and R1 resections that required an unplanned readmission for tumor bed re-excision (0% vs. 37.1% after SS, p = 0.002). VT required more total surgeries (2.8 ± 0.9 vs. 1.8 ± 0.9 for SS, p = 0.0002). Post-operative infectious and wound complications were equivalent (Table). Conclusions: Our VAC temporization strategy had a significantly lower LR than SS treatment. While high quality multi-institutional validation is required, VT may represent a paradigm shift in the management of myxofibrosarcoma.[Table: see text]
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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 | 0.000 | 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) | 0.002 | 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".