Children's Oncology Group's 2023 blueprint for research: Myeloid neoplasms
Bibliographic record
Abstract
During the past decade, the outcomes of pediatric patients with acute myeloid leukemia (AML) have plateaued with 5-year event-free survival (EFS) and overall survival (OS) of approximately 46 and 64%, respectively. Outcomes are particularly poor for those children with high-risk disease, who have 5-year OS of 46%. Substantial survival improvements have been observed for a subset of patients treated with targeted therapies. Specifically, children with KMT2A-rearranged AML and/or FLT3 internal tandem duplication (FLT3-ITD) mutations benefitted from the addition of gemtuzumab ozogamicin, an anti-CD33 antibody-drug conjugate, in the AAML0531 clinical trial (NCT00372593). Sorafenib also improved response and survival in children with FLT3-ITD AML in the AAML1031 clinical trial (NCT01371981). Advances in characterization of prognostic cytomolecular events have helped to identify patients at highest risk of relapse and facilitated allocation to consolidative hematopoietic stem cell transplant (HSCT) in first remission. Some patients clearly have improved survival with HSCT, although the benefit is largely unknown for most patients. Finally, data-driven refinements in supportive care recommendations continue to evolve with meaningful and measurable reductions in toxicity and improvements in EFS and OS. As advances in application of targeted therapies, risk stratification, and improved supportive care measures are incorporated into current trials and become standard-of-care, there is every expectation that we will see improved survival with a reduction in toxic morbidity and mortality. The research agenda of the Children's Oncology Group's Myeloid Diseases Committee continues to build upon experience and outcomes with an overarching goal of curing more children with AML.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.176 | 0.130 |
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".