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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".