Impact of Tyrosine Kinase Inhibitors (TKIs) on Growth in Children and Adolescents with Chronic Myeloid Leukemia: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Chronic Myeloid Leukemia (CML) is a rare myeloproliferative disease in childhood. Treatment in CML includes Tyrosine Kinase Inhibitors (TKIs), which inhibit the cytoplasmic kinase BCR/ABL. Tyrosine kinases play a key role in the secretion of growth hormone and insulin-like growth factor 1 (IGF-1). OBJECTIVE: The aim of this systematic review was to study the effect of TKIs on the growth of children and adolescents with CML. METHODS: English-language publications were searched in the PubMed/Cochrane library/Google Scholar databases (2002-2023), and retrieved studies were assessed according to PRISMA-Statement and Newcastle- Ottawa-scale. RESULTS: The search strategy yielded 1066 articles. After applying the inclusion/exclusion criteria, 941 were excluded based on title screening and 111 on abstract review. The systematic review included 14 articles (11 retrospective observational studies/3 clinical trials). Twelve studies reported data on the prevalence of growth disorders after the administration of 1st generation TKIs (imatinib). Two studies reported a negative effect of 2nd generation TKIs (dasatinib/nilotinib) on physical growth. Four studies recorded a decrease in height z-score after treatment compared to baseline. Two 1st-generation TKIs studies reported data on children's final height; one reported restoration of final height to normal after the onset of puberty, despite initial slowing, and the final height was lower than mid-parental target height. Serum IGF-1 levels were reported in 2 studies to be within normal range, while in 3 studies, a significant decrease was documented. Considerable study heterogeneity was observed related to dosage/duration of treatment/disease phase/stage of puberty/ethnicity. CONCLUSION: A negative effect of TKIs on the growth and final height of children was noted.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".