Retroperitoneal sarcoma: the Transatlantic Australasian Retroperitoneal Sarcoma Working Group Program
Bibliographic record
Abstract
PURPOSE OF REVIEW: The Transatlantic Australasian Retroperitoneal Sarcoma Working Group (TARPSWG) is a bottom-up clinical network established in 2013 with the goal of improving the care and outcomes of patients with retroperitoneal sarcoma (RPS). Here, we review the knowledge produced by this collaborative effort and examine the future potential of this group. RECENT FINDINGS: TARPSWG has produced retrospective studies focused on patients with primary and recurrent RPS allowing a better understanding of patient prognosis, treatment outcomes and tumor biology. The group has played a pivotal role in a phase III randomized STudy of preoperative RAdiotherapy plus Surgery versus surgery alone for patients with Retroperitoneal Sarcoma (STRASS) trial, favoring patient recruitment and trial completion. A prospective registry for patients with primary RPS populated by TARPSWG members is ongoing. TARPSWG has created consensus papers with recommendations regarding the management of patients with primary, recurrent and metastatic RPS that collated the views of representatives of sarcoma centers from Europe, North America, Asia and Australia. SUMMARY: Since its inception, TARPSWG has become a leading network in the field of RPS. It has made a major contribution to the world of RPS research and cares allowing to overcome the limitations related to the rarity of the disease through collaboration.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".