Bibliographic record
Abstract
Soft tissue sarcomas (STS) are rare malignancies. STS represent a heterogeneous group of tumors, with many of them posing a high risk of local recurrence and distant metastasis. The major therapeutic goals of treating STS are to maximize local tumor control using minimal surgery and to improve survival. The ability of radiation therapy (RT) to improve local control has made it a cornerstone in the multimodality treatment of STS. Koshy reported survival benefit of RT in patients undergoing limbsparing surgery for soft tissue sarcomas of the extremities. A retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database that included data from 6,960 patients. They reports that radiation was associated with improved survival in patients with high-grade tumors. The randomized study of preoperative versus postoperative radiation therapy conducted by the National Cancer Institute of Canada (NCIC). The radiation therapy techniques consisted of 50 Gy in the preoperative setting and 66 Gy given postoperatively. Patients treated with postoperative radiation therapy tended to have greater fibrosis. Fibrosis, joint stiffness and edema adversely affect patient function. Haas reported the proposed consensus guidelines on target volume delineation with RT for STS in 2012. Particle therapy, intensity-modulated RT (IMRT) and image-guided RT (IGRT) offers the opportunity to reduce the normal tissue morbidity of RT while maintaining local tumor control. In conclusion, the precision with which the radiation dose is distributed with advanced RT has a beneficial effect in sparing normal tissue with improved local control over that achieved with conventional RT.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".