Roadmap for the next generation of Children’s Oncology Group rhabdomyosarcoma trials
Bibliographic record
Abstract
Clinical trials conducted by the Intergroup Rhabdomyosarcoma (RMS) Study Group and the Children's Oncology Group have been pivotal to establishing current standards for diagnosis and therapy for RMS. Recent advancements in understanding the biology and clinical behavior of RMS have led to more nuanced approaches to diagnosis, risk stratification, and treatment. The complexities introduced by these advancements, coupled with the rarity of RMS, pose challenges to conducting large-scale phase 3 clinical trials to evaluate new treatment strategies for RMS. Given these challenges, systematic planning of future clinical trials in RMS is paramount to address pertinent questions regarding the therapeutic efficacy of drugs, biomarkers of response, treatment-related toxicity, and patient quality of life. Herein, the authors outline the proposed strategic approach of the Children's Oncology Group Soft Tissue Sarcoma Committee to the next generation of RMS clinical trials, focusing on five themes: improved novel agent identification and preclinical to clinical translation, more efficient trial development and implementation, expanded opportunities for knowledge generation during trials, therapeutic toxicity reduction and quality of life, and patient engagement.
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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 | 0.187 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".