Children's Oncology Group's 2023 blueprint for research: Non‐Hodgkin lymphoma
Bibliographic record
Abstract
Pediatric non-Hodgkin lymphoma (NHL) includes over 30 histologies (many with subtypes), with approximately 800 cases per year in the United States. Improvements in survival in NHL over the past 5 decades align with the overall success of the cooperative trial model with dramatic improvements in outcomes. As an example, survival for advanced Burkitt lymphoma is now >95%. Major remaining challenges include survival for relapsed and refractory disease and long-term morbidity in NHL survivors. Langerhans cell histiocytosis (LCH) was added to the NHL Committee portfolio in recognition of LCH as a neoplastic disorder and the tremendous unmet need for improved outcomes. The goal of the Children' Oncology Group NHL Committee is to identify optimal cures for every child and young adult with NHL (and LCH). Further advances will require creative solutions, including engineering study groups to combine rare populations, biology-based eligibility, alternative endpoints, facilitating international collaborations, and coordinated correlative biology.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.055 | 0.037 |
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".