PREOPERATIVE RADIOTHERAPY AND POSTOPERATIVE COMPLICATIONS AMONG PATIENTS WITH PRIMARY RETROPERITONEAL SARCOMA: A POST HOC ANALYSIS OF THE EORTC-62092 (STRASS) PHASE 3 RANDOMIZED CLINICAL TRIAL
Bibliographic record
Abstract
OBJECTIVE: To assess the association between preoperative radiotherapy (RT) and major postoperative complications in patients with primary retroperitoneal sarcoma (RPS). BACKGROUND: The effect of preoperative RT on postoperative morbidity in patients undergoing surgery for primary RPS remains uncertain. METHODS: In the post hoc analysis, analyses were performed for the entire STRASS cohort and for the subgroup with well-differentiated liposarcoma (WDLPS) and grade 1-2 (G1-G2) dedifferentiated liposarcoma (DDLPS). The exploratory endpoint was the occurrence of major postoperative complications (Clavien-Dindo grade ≥3) within 60 days of surgery, evaluated according to the treatment arm (surgery alone or preoperative RT followed by surgery). Logistic regression models were used to identify whether treatment arm was independently associated with major postoperative complications. RESULTS: A total of 242 patients (124 [51.2%] male; median age, 61 years [IQR, 52-68]) were included. Overall, 128 patients were randomized to surgery alone and 114 to preoperative RT plus surgery. In the WDLPS/G1-G2 DDLPS subgroup (n=122), 59 underwent surgery alone and 63 received preoperative RT. The overall incidence of major postoperative complications was 19.8%, with no significant difference between treatment arms (19.5% surgery alone vs 20.2% preoperative RT; P = 0.90). On multivariable analysis, preoperative RT was not associated with increased odds of major postoperative complications (OR, 0.95; 95% CI, 0.48-1.87; P = 0.88). Findings were consistent in the WDLPS/G1-G2 DDLPS subgroup (OR, 1.57; 95% CI, 0.56-4.41; P = 0.39). CONCLUSIONS: These data suggest that concerns regarding postoperative complications should not preclude the use of preoperative RT when clinically indicated.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".