Evidence-Based Recommendations for Local Therapy for Soft Tissue Sarcomas
Bibliographic record
Abstract
There has been a gradual migration in the local treatment of soft tissue sarcomas from amputation and similar radical resectional approaches to more conservative, function-preserving surgery combined with radiotherapy. This progress has been made possible by small, single-institution, randomized trials that demonstrated the superiority of this more conservative, combined-modality approach. In the new millennium, attention has shifted to defining subsets of patients who might be adequately treated by surgery alone and defining the optimal sequence of surgery and radiation for patients who require both types of local therapy. There remains considerable discussion and debate surrounding the issue of pre- and postoperative chemotherapy for patients with localized soft tissue sarcomas. Adjuvant chemotherapy is a standard of care for adults who have the subtypes of soft tissue sarcomas that typically occur in pediatric patients (Ewing sarcoma, rhabdomyosarcoma), and just as clearly, adjuvant chemotherapy is not warranted in patients with low- and intermediate-risk disease (stages I and II). For patients with higher risk disease (stage III), the available randomized trials do not convincingly demonstrate a clinical benefit to adjuvant chemotherapy. As such, a complete accounting of potential risks and benefits is appropriate when discussing adjuvant chemotherapy with patients who have stage III disease.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".