Intraoperative radiation therapy for high risk soft tissue sarcoma resection margins
Bibliographic record
Abstract
Background and objectives: External beam radiation (EBRT) can reduce local recurrence (LR) of soft tissue sarcomas (STS). The addition of intraoperative radiation therapy (IORT) can deliver high dose radiation boost to anticipated “high risk” margins while sparing adjacent structures. Methods: A retrospective review (2004-2012) was performed of STS treated with surgical resection and IORT using HDR brachytherapy for anticipated close/positive margins. Results: Twenty-four patients underwent 25 resections with IORT (1 patient had 2 separate recurrences). Tumors were primary in 72%, deep in 96% and intermediate/high grade in 84%. Tumor locations were extremity (44%), retroperitoneal (40%), truncal (12%) and neck (4%). Common histologies included pleomorphic (32%), liposarcoma (12%) and myxofibrosarcoma (12%). Neoadjuvantly, 3 received chemotherapy and 13 received EBRT (median 50Gy; range 45-54). Median IORT dose was 12Gy (range 10-17.5). Margins were microscopically positive in 20%; none were grossly positive. Adjuvantly, 5 received EBRT (median 46Gy, range 45-50) and 2 received chemotherapy. Median follow-up was 20.1 months (range 2.7-96.4). No recurrences occurred at the IORT sites. Two-year LR free survival was 60.8% and disease specific survival was 84%. Conclusion: Use of IORT at time of STS resection was effective at preventing LR at the treated site despite “high risk” features.
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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".