New research strategies in retroperitoneal sarcoma. The case of TARPSWG, STRASS and RESAR: making progress through collaboration
Bibliographic record
Abstract
PURPOSE OF REVIEW: Retroperitoneal sarcoma (RPS) is a rare disease, and until recently, its natural history and outcome were poorly understood. Recently, collaborations between individual centers have led to an unprecedented collection of retrospective and prospective data and successful recruitment to the first randomized trial as described here. RECENT FINDINGS: A debate about the beneficial role of extended surgery in RPS triggered an initial collaboration between Europe and North America, the TransAtlantic RetroPeritoneal Sarcoma Working Group (TARPSWG). This collaboration has been instrumental in harmonizing the surgical approach among expert centers, characterizing the pattern of postresection failure of the different histological subtypes, identifying new ways to stage RPS and testing the role of preoperative radiotherapy in a randomized fashion (STRASS-1 study). The collaboration has now expanded to include centers from Asia, Australia and South America. A prospective registry has been started and a new randomized trial, STRASS-2, is in preparation to analyze the role of neoadjuvant chemotherapy for high-grade liposarcoma and leiomyosarcoma of the retroperitoneum. SUMMARY: Collaboration is critical to study a rare disease like RPS. Both retrospective and prospective data are useful to improve knowledge, generate hypotheses and build evidence to test, whenever possible, in clinical trials.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".