Relapsed pediatric acute myeloid leukaemia: state-of-the-art in 2023
Bibliographic record
Abstract
Although outcomes of children and adolescents with newly diagnosed acute myeloid leukemia (AML) have improved significantly over the past two decades, more than one-third of patients continue to relapse and experience suboptimal long-term outcomes. Given the small numbers of patients with relapsed AML and historical logistical barriers to international collaboration including poor trial funding and drug availability, the management of AML relapse has varied among pediatric oncology cooperative groups with several salvage regimens utilized and a lack of universally defined response criteria. The landscape of relapsed pediatric AML treatment is changing rapidly, however, as the international AML community harnesses collective knowledge and resources to characterize the genetic and immunophenotypic heterogeneity of relapsed disease, identify biological targets of interest within specific AML subtypes, develop new precision medicine approaches for collaborative investigation in early-phase clinical trials, and tackle challenges of universal drug access across the globe. This review provides a comprehensive overview of progress achieved to date in the treatment of pediatric patients with relapsed AML and highlights modern, state-of-the-art therapeutic approaches under active and emerging clinical investigation that have been facilitated by international collaboration among academic pediatric oncologists, laboratory scientists, regulatory agencies, pharmaceutical partners, cancer research sponsors, and patient advocates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".