Progress in Retroperitoneal Sarcoma Management: Surgical and Radiotherapy Approaches
Bibliographic record
Abstract
Surgical resection is the cornerstone of curative treatment for retroperitoneal sarcomas (RPS), aiming for complete excision, yet the complexity of RPS with its proximity to vital structures continues to lead to high local recurrence rates after surgery alone. Thus, the role of radiotherapy (RT) continues to be refined to improve local control, which remains an important goal to prevent RPS recurrence. The recently completed global randomized trial to evaluate the role of surgery with and without preoperative RT - STRASS1, did not demonstrate a significant overall benefit for neoadjuvant RT based on the pre-specified definition of abdominal recurrence-free survival, however, sensitivity analysis using a standard definition of local recurrence and analysis of outcomes by compliance to the RT protocol suggests histology-specific benefit in well- and some de-differentiated liposarcomas. Ultimately, multidisciplinary collaboration and personalized approaches that consider histological sarcoma types and patient-specific factors are imperative for optimizing the therapeutic strategy in the management 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".