Recent Progress in the Management of Retroperitoneal Sarcoma
Bibliographic record
Abstract
Retroperitoneal sarcomas (RPS) are rare tumours that typically present late and carry a poor prognosis even following grossly complete resection. In an attempt to improve the outlook for patients with RPS, sarcoma specialists have employed various adjuvant therapies, including extermal beam radiation, intraoperative radiation, brachyradiation and systemic chemotherapy. This article reviews the presentation and prognosis of RPS, and focuses on the results of new treatment strategies compared with conventional management.A Medline search of the English literature was performed to identify all retrospective and prospective reports relating to the management of adult RPS published since 1980. Series that did not analyse RPS separately from other intra-abdominal or extra-abdominal sarcomas or other malignancies were excluded, and information on investigation, presentation, prognostic factors, treatment and outcome was extracted from the remaining reports. Survival and local control data were collected from reports that contained at least 30 cases of RPS (n = 31).While surgical resection remains the cornerstone of treatment for RPS, the majority of patients will relapse and die from sarcoma within 5 years of resection. Adjuvant radiation may improve these results, but further trials are required to definitively demonstrate its benefit. Possible reasons for the failure of conventional treatment are discussed, and alternative strategies designed to overcome these obstacles are presented.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".