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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".