Proceedings of the Comprehensive Oncology Network Evaluating Rare CNS Tumors (NCI-CONNECT) Oligodendroglioma Workshop
Bibliographic record
Abstract
BACKGROUND: Oligodendroglioma is a rare primary central nervous system (CNS) tumor with highly variable outcome and for which therapy is usually not curative. At present, little is known regarding the pathways involved with progression of oligodendrogliomas or optimal biomarkers for stratifying risk. Developing new therapies for this rare cancer is especially challenging. To overcome these challenges, the neuro-oncology community must be particularly innovative, seeking multi-institutional and international collaborations, and establishing partnerships with patients and advocacy groups thereby ensuring that each patient enrolled in a study is as informative as possible. METHODS: The mission of the National Cancer Institute's NCI-CONNECT program is to address the challenges and unmet needs in rare CNS cancer research and treatment by connecting patients, health care providers, researchers, and advocacy organizations to work in partnership. On November 19, 2018, the program convened a workshop on oligodendroglioma, one of the 12 rare CNS cancers included in its initial portfolio. The purpose of this workshop was to discuss scientific progress and regulatory challenges in oligodendroglioma research and develop a call to action to advance research and treatment for this cancer. RESULTS: The recommendations of the workshop include a multifaceted and interrelated approach covering: biology and preclinical models, data sharing and advanced molecular diagnosis and imaging; clinical trial design; and patient outreach and engagement. CONCLUSIONS: The NCI-CONNECT program is well positioned to address challenges in oligodendroglioma care and research in collaboration with other stakeholders and is developing a list of action items for future initiatives.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".