Children's Oncology Group's 2023 blueprint for research: Soft tissue sarcomas
Bibliographic record
Abstract
In the United States, approximately 850-900 children and adolescents each year are diagnosed with soft tissue sarcomas (STS). STS are divided into rhabdomyosarcoma (RMS) and non-rhabdomyosarcoma STS (NRSTS). RMS and NRSTS are risk stratified into low-, intermediate-, and high-risk categories, with 5-year survival rates of approximately 90%, 50%-70%, and 20%, respectively. Recent key achievements from the Children's Oncology Group (COG) STS Committee include the identification of new molecular prognostic factors for RMS, development and validation of a novel risk stratification system for NRSTS, successful completion of a collaborative NRSTS clinical trial with adult oncology consortia, and collaborative development of the INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT). Current COG trials for RMS are prospectively evaluating a new risk stratification system that incorporates molecular findings, de-intensification of therapy for a very low-risk subgroup, and augmented therapy approaches for intermediate- and high-risk RMS. Trials for NRSTS exploring novel targets and local control modalities are in development.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.078 | 0.050 |
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".