Phase 2 study of preoperative image‐guided intensity‐modulated radiation therapy to reduce wound and combined modality morbidities in lower extremity soft tissue sarcoma
Bibliographic record
Abstract
BACKGROUND: This study sought to determine if preoperative image-guided intensity-modulated radiotherapy (IG-IMRT) can reduce morbidity, including wound complications, by minimizing dose to uninvolved tissues in adults with lower extremity soft tissue sarcoma. METHODS: The primary endpoint was the development of an acute wound complication (WC). IG-IMRT was used to conform volumes to avoid normal tissues (skin flaps for wound closure, bone, or other uninvolved soft tissues). From July 2005 to June 2009, 70 adults were enrolled; 59 were evaluable for the primary endpoint. Median tumor size was 9.5 cm; 55 tumors (93%) were high-grade and 58 (98%) were deep to fascia. RESULTS: Eighteen (30.5%) patients developed WCs. This was not statistically significantly different from the result of the National Cancer Institute of Canada SR2 trial (P = .2); however, primary closure technique was possible more often (55 of 59 patients [93.2%] versus 50 of 70 patients [71.4%]; P = .002), and secondary operations for WCs were somewhat reduced (6 of 18 patients [33%] versus 13 of 30 patients [43%]; P = .55). Moderate edema, skin, subcutaneous, and joint toxicity was present in 6 (11.1%), 1 (1.9%), 5 (9.3%), and 3 (5.6%) patients, respectively, but there were no bone fractures. Four local recurrences (6.8%, none near the flaps) occurred with median follow-up of 49 months. CONCLUSIONS: The 30.5% incidence of WCs was numerically lower than the 43% risk derived from the National Cancer Institute of Canada SR2 trial, but did not reach statistical significance. Preoperative IG-IMRT significantly diminished the need for tissue transfer. RT chronic morbidities and the need for subsequent secondary operations for WCs were lowered, although not significantly, whereas good limb function was maintained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".