A Meta-Analysis of the Association Between Radiation Therapy and Survival for Surgically Resected Soft-Tissue Sarcoma
Bibliographic record
Abstract
OBJECTIVES: Radiotherapy for soft-tissue sarcoma (STS) has been shown to reduce local recurrence, but without clear improvement in survival. We conducted a meta-analysis to study the association between radiotherapy and survival in patients undergoing surgery for STS. METHODS: A systematic review was conducted from PubMed, EMBASE, Web of Science, and Cochrane databases. Our population of interest consisted of adults with primary extremity, chest wall, trunk, or back STS. Our metameters were either an odds or hazard ratio for mortality. A bias score was generated for each study based on margin status and grade. RESULTS: Of 1044 studies, 30 met inclusion criteria for final analysis. The pooled odds ratio in patients receiving radiation was 0.94 (95% confidence interval [CI], 0.78-1.14). The pooled estimate of the hazards ratio in patients receiving radiation was 0.87 (95% CI, 0.73-1.03) overall and 0.65 (95% CI, 0.52-0.82) for studies judged to be at low risk of bias. Significant publication bias was not seen. CONCLUSIONS: High-quality studies reporting adjusted hazard ratios are associated with improved survival in patients receiving radiotherapy for STS. Studies in which odds ratios are calculated from event data and those that do not report adjusted outcomes do not show the same association, likely due to confounding by indication.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".