Abstract B048: The Ewing Sarcoma Institute – a catalyst to improve outcomes for patients with Ewing sarcoma globally
Bibliographic record
Abstract
Abstract Ewing sarcoma (EwS) is an aggressive bone and soft-tissue cancer that mainly affects children, adolescents, and young adults. While current multi-modal treatments offer a 70–80% survival rate for patients with localized tumors, the prognosis remains poor for patients with metastatic or relapsed disease and EwS survivors suffer from late effects and life-long morbidity. Therapeutic progress in EwS has been hampered by the rarity of the disease (1-3 per million), underfunding and fragmentation of research, scattered and limited access to data and to novel therapeutic compounds. To overcome these bottlenecks, leading experts from around the world in community with patients, families and advocates and supported by a catalytic philanthropic donation have founded the virtual Ewing Sarcoma Institute (ESI; http://www.ewingsarcoma.org). As an unprecedented collaborative platform that transcends geographical boundaries and institutional silos, ESI is the first and so far only initiative providing structure to all shareholders of the international EwS community. ESI is dedicated to radically improving outcomes for EwS patients by uniting the global community to accelerate collaborative scientific discovery and development of breakthrough treatments. ESI shall serve as a catalyst to accelerate research, expand clinical trials, and ensure every EwS patient has access to the best possible care and emerging therapies. It unites the global research community to establish a strategic, high-impact research agenda and empower patients and families with knowledge and support. The goal is to learn from every individual EwS patient. Therefore, ESI will focus on several critical areas to achieve its mission. ● ESI will facilitate collaborative research projects that leverage the combined expertise of international teams, with emphasis on understanding the molecular mechanisms driving EwS and identifying novel therapeutic targets. ● ESI will streamline the development and implementation of multi-institutional clinical trials, enabling faster patient enrollment and more robust data collection across diverse populations. ● Critically, ESI will create secure, standardized systems for sharing clinical, genomic, and research data, accelerating the pace of discovery while maintaining patient privacy. ● In all of these efforts, ESI will ensure that patient voices remain central to research priorities and treatment development, while providing resources and support for families navigating the challenge of an EwS diagnosis. The launch of ESI in July 2025 represents not just an organizational milestone, but a call to action for the entire global community. Researchers, clinicians, patients, families, philanthropists, and policymakers are invited to join this critical mission. Citation Format: Heinrich Kovar, James Amatruda, Carol Basso, Chad Brown, Rashmi Chugh, Olivier Delattre, Julia Glade Bender, Thomas Grunewald, Lee Helman, Elizabeth R Lawlor, Pei Pei Ma, Charles Nearburg, Poul Sorensen, Kimberly Stegmaier, Sandra Strauss, Jeffrey Toretsky, Patrick Grohar. The Ewing Sarcoma Institute – a catalyst to improve outcomes for patients with Ewing sarcoma globally [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr B048.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.066 | 0.031 |
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".