Neoadjuvant Radiotherapy Followed by Surgery Compared with Surgery Alone in the Treatment of Retroperitoneal SarcomA: A Population-Based Comparison
Bibliographic record
Abstract
Introduction: Retroperitoneal sarcoma (rps) encompasses a heterogeneous group of malignancies with a high recurrence rate after resection. Neoadjuvant radiotherapy (nrt) is often used in the hope of sterilizing margins and decreasing local recurrence after excision. We set out to compare local recurrence-free survival (lrfs) and overall survival (os) in patients treated with or without nrt before resection. Methods: Patients diagnosed with rps from February 1990 to October 2014 were identified in the Alberta Cancer Registry. Patients with complete gross resection of rps and no distant disease were included. Patient, tumour, treatment, and outcomes data were abstracted in a primary chart review. Baseline characteristics were compared using the Wilcoxon nonparametric test for continuous data and the Fisher exact test for dichotomous and categorical data. Survival was analyzed using Kaplan–Meier curves with log-rank test. Cox regression was performed to control for age, sex, tumour size, tumour grade, date of diagnosis, multivisceral resection, and intraoperative rupture. Results: Resection alone was performed in 62 patients, and resection after nrt, in 40. Use of nrt was associated with multivisceral resection and negative microscopic margins. On univariate analysis, nrt was associated with superior median lrfs (89.3 months vs. 28.4 months, p = 0.04) and os (119.4 months vs. 75.9 months, p = 0.04). On multivariate analysis, nrt, younger age, and lower tumour grade predicted improved lrfs and os; sex, tumour size, date of diagnosis, multivisceral resection, and tumour rupture did not. Conclusions: In this population-based study, nrt was associated with superior lrfs and os on both univariate and multivariate analysis. When feasible, nrt should be considered until a randomized controlled trial is completed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".