Adjuvant Chemotherapy Following Complete Resection of Soft Tissue Sarcoma in Adults: A Clinical Practice Guideline
Bibliographic record
Abstract
Purpose. To review the literature and make recommendations for the use of anthracycline-based adjuvant chemotherapy in adult patients with soft tissue sarcoma (STS).Patients. The recommendations apply to patients >15 years old with completely resected STS.Methods. A systematic overview of the published literature was combined with a consensus process around the interpretation of the evidence in the context of conventional practice to develop an evidence-based practice guideline.Results. Four meta-analyses and 17 randomized clinical trials comparing anthracycline-based adjuvant chemotherapy versus observation were reviewed. The Sarcoma Meta-Analysis Collaboration (SMAC) was the best analysis because it assessed individual patient data and had the longest follow-up. The results of the SMAC meta-analysis together with data from more recently published randomized trials, as well as our analysis of the toxicity and compliance data, are incorporated in this systematic review.Discussion. It is reasonable to consider anthracycline-based adjuvant chemotherapy in patients who have had removal of a sarcoma with features predicting a high likelihood of relapse (deep location, size >5 cm, high histological grade). Although the benefits of adjuvant chemotherapy are most apparent in patients with extremity sarcomas, patients with high-risk tumours at other sites should also be considered for such therapy.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".