Patterns of Practice Survey: Radiotherapy for Soft Tissue Sarcoma of the Extremities
Bibliographic record
Abstract
Background Neoadjuvant or adjuvant radiotherapy (RT) for extremity soft tissue sarcoma (STS) confers significant local control benefit. To determine patterns of practice, a survey of RT planning practices was undertaken. Method Members of the Connective Tissue Oncology Society and Canadian Association of Radiation Oncology participated in this survey pertaining to general practice patterns of RT for extremity STS, patterns of contouring and planning, and use of quality control measures such as guidelines, tumor boards, and quality assurance rounds. Results A total of 58 radiation oncologists treating extremity STS from 12 countries responded. 89.7% work in academically affiliated centres, and 55.2% saw at least 20 cases of extremity STS per year. Most (96.7%) had access to multidisciplinary sarcoma boards (85.5% of those discussed every referred sarcoma case). 78.6% held quality assurance rounds. Most (92.9%) used planning guidelines. Pre-operative RT was used nearly twice as much as post-operative RT. CT simulation with MR fusion was used by 94.6%. Patterns of clinical target volume (CTV) contouring for both superficial and deep STS were variable. 69.8% contoured a normal soft tissue strip for extremity sarcoma, 13.5% without routine constraints and the remainder with various constraints. Most (91.1%) used 50 Gy in 25 fractions pre-operatively and 39.6% reported using post-operative RT boost for positive margins. Post-operative dose was more variable from 59.4 Gy to 70 Gy. Conclusion Major aspects of RT planning for extremity STS were similar among the responders, and most were academically affiliated. Over twice as many employed pre-operative as opposed to post-operative RT. There was considerable heterogeneity in use of: margins for contouring, normal soft tissue strip as an avoidance structure, and boost for positive margins. This survey shows variable patterns of practice and identifies areas that may require further research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".