Twenty Years on: What Do We Really Know about Ewing Sarcoma and What Is the Path Forward?
Bibliographic record
Abstract
Ewing sarcoma (ES) is a highly aggressive bone and soft-tissue tumor with peak incidence among adolescents and young adults. Despite advances in local control and systemic chemotherapy, metastatic relapse after an initial clinical remission remains a significant clinical problem. In addition, metastasis at the time of presentation or at relapse continues to be the leading cause of death for patients diagnosed with ES. Since the discovery of the pathognomonic EWS-FLI1 fusion gene more than 20 years ago, much about the molecular and cellular biology of ES pathogenesis has been learned. In addition, more recent exploitation of advances in stem cell and developmental biology has provided key insights into the cellular origins of ES and the role of epigenetic deregulation in tumor initiation and maintenance. Nevertheless, the mechanisms that drive tumor relapse and metastasis remain largely unknown. These gaps in our knowledge continue to hamper the development of novel therapeutic strategies that may improve outcomes for patients with relapsed and metastatic disease. In this article we review the current status of ES biology research, highlighting areas of investigation that we consider to have the greatest potential to yield findings that will translate into clinically significant advances.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".