Surveillance Post Surgery for Retroperitoneal Soft Tissue Sarcoma
Bibliographic record
Abstract
Complete en bloc surgical resection offers the best opportunity for the cure of primary retroperitoneal sarcomas (RPS). The potential for disease recurrence, in the form of both loco-regional recurrence and distant metastases, underpins the rationale for postoperative surveillance. There is a paucity of high-quality evidence underpinning follow-up for RPS patients, and most practice guidelines draw from expert opinion and evidence from soft tissue sarcomas of the extremities. The available observational retrospective data analysis has failed to demonstrate that high-intensity radiological surveillance improves the overall survival in patients. The lack of a robust evidence base has given rise to variations in approaches to post-operative surveillance strategies adopted by specialist centres managing RPS across the world. More high-quality prospective research is needed and planned to more clearly support surveillance approaches that balance oncologic outcomes, patient-centric care, and health service value. Risk stratification tools exist and are available for use in routine practice. Their use will likely support more individualised post-operative surveillance moving forward. Surveillance will likely be underpinned by serial radiological imaging for the medium term. However, developments in genomics offer hope for biomarkers such as ctDNA to impact patient care positively in the future and further support individualised patient care pathways.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".