Optimizing Ewing Sarcoma and Osteosarcoma Biopsy Acquisition: A Children’s Oncology Group Bone Tumor Committee Consensus Statement
Bibliographic record
Abstract
Trends in diagnostic biopsy sample collection approaches for primary bone sarcomas have shifted in the past 2 decades. Although open/incisional biopsies used to be the predominant approach to obtain diagnostic material for Ewing sarcoma and osteosarcoma, image-guided core needle biopsies have increased in frequency and are safe for patients. These procedures are less invasive and reduce recovery times but have potential limitations. The quantity and quality of tissue obtained through these procedures vary between institutions. Acquired viable tissue volumes can be low, limiting the conduct of downstream expanded clinical workup, molecular analyses, and research. Patients with advanced Ewing sarcoma and osteosarcoma continue to have overall poor outcomes despite dose-intensive cytotoxic chemotherapy. The biology of treatment resistance is not currently well understood, partly due to limited availability of relevant tissue to study. There is a need for access to quality tumor specimens for molecular and other analyses to identify high-risk tumor subsets and drive discovery to improve patient outcomes. Given broad variability in bone tumor tissue procurement and processing across member institutions, the Children's Oncology Group Bone Tumor Committee convened a multidisciplinary group of experts to outline the current and near-future tissue needs for optimal clinical care and access to research platforms. The goal of this working group was to provide high-level guidance on biopsy practices that safely meet these evolving needs. Harmonizing tissue collection practices is paramount to improving the care of children, adolescents, and young adults diagnosed with Ewing sarcoma and osteosarcoma.
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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 | 0.089 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".