Controversies in the Treatment of CML in Children and Adolescents: TKIs versus BMT?
Bibliographic record
Abstract
Chronic myeloid leukemia (CML) is a relatively rare hematopoietic malignancy in the pediatric and adolescent population. This makes it difficult to perform clinic trials that can define the best therapeutic option when considering the impact of tyrosine kinase inhibitors (TKIs) versus the established approach of allogeneic hematopoietic cell transplantation (HCT). With the relatively low toxicity of TKIs, there are little data regarding when HCT or long-term TKI therapy is a better option. There are even less data regarding the duration of TKI treatment in the pediatric CML in chronic phase (CML-CP) patients who may receive over 60 years of therapy. As children and adolescent are treated for longer times with TKIs, it has become clear that toxicities may make long-term TKI therapy less attractive compared to allogeneic HCT. HCT has the long-term complications of growth failure, infertility, chronic graft-versus-host disease (GVHD), metabolic syndrome, and secondary malignancies, whereas prolonged TKIs may cause growth failure, hepatic, and cardiac complications. Moreover, HCT is a potentially curative intervention, whereas TKI is not curative, requiring prolonged exposure. In this article, we discuss the relative merit of the 2 therapeutic approaches and recommend that all children and adolescents with CML-CP should initially be treated with imatinib and maintained with TKI therapy indefinitely if there is a good response. We recommend that allogeneic HCT with an HLA-identical sibling donor or closely matched unrelated donor be considered for patients with treatment failure or recurrence after receiving salvage second-generation TKI treatment. We also conclude that randomized international trials are urgently needed to evaluate the best therapies for pediatric CML.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 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".