Bibliographic record
Abstract
Overview The management of soft tissue sarcomas is driven by the anatomic site and histology of the primary and is increasingly affected by the specific genetics of the sarcoma. In this chapter, we focus on the principles of management of this group of over 50 cancer subtypes to highlight commonalities and differences from anatomical constraints of surgery to specifics of adjuvant radiation to identification of systemic therapeutics that are appropriate for each histology. We will discuss in this chapter the etiology, presentation, diagnosis, staging, and multidisciplinary management of patients with sarcomas of soft tissue. Surgery remains paramount to achieve cure for the vast majority of sarcomas. Radiation therapy is used for larger tumors in the appropriate clinical context. The evolution of increasingly sophisticated radiation techniques is highlighted in this chapter. As pertains to systemic therapy, this chapter is written at a time in which first‐line therapies for soft tissue sarcomas may change, raising anew some questions of adjuvant therapy that remain incompletely answered. Where appropriate, we attempt to link specific histologies or molecular changes to therapeutic suggestions, with the understanding and hope that novel agents will supplant the medications that are available but that have not materially affected outcomes for few diagnoses other than GIST in the past several years.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".