Next questions in the management of retroperitoneal sarcoma
Bibliographic record
Abstract
PURPOSE OF REVIEW: Retroperitoneal soft-tissue sarcomas (RPS) are a group of rare, histologically distinct tumours with variable recurrence patterns depending on histological type. This review will discuss the growing body of evidence supporting histology-specific, multidisciplinary management and highlight areas of future research for patients with RPS. RECENT FINDINGS: Histology-tailored surgery is the cornerstone of management in patients with localized RPS. Further efforts to develop resectability criteria and identify patients who will benefit from neoadjuvant treatment strategies will help standardize the treatment of patients with localized RPS. Surgery for local recurrence is well tolerated in selected patients and re-iterative surgery in liposarcoma (LPS) may be beneficial at the time of local recurrence. The management of advanced RPS holds promise with several trials currently investigating systemic treatment beyond conventional chemotherapy. SUMMARY: The management of RPS has made significant progress over the past decade owing to international collaboration. Ongoing efforts to identify patients who will derive the most benefit from all treatment strategies will continue to advance the field of RPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".