Long-term safety review of tyrosine kinase inhibitors in chronic myeloid leukemia - What to look for when treatment-free remission is not an option
Bibliographic record
Abstract
The development of BCR::ABL1-targeting tyrosine kinase inhibitors (TKIs) has improved the prognosis of patients with chronic myeloid leukemia (CML). Although there are some common class-wide side effects, differences in safety profiles between TKIs allow physicians and patients to personalize treatment plans. Treatment selection depends on several factors, such as age, disease risk, comorbidities, and concomitant medications. In second- and later-line settings, response to previous TKIs and mutation analyses should also be used to guide TKI selection. Several strategies can be used to manage adverse events (AEs) that emerge during treatment, e.g., dose reductions/interruptions, monitoring, treatment of AEs, lifestyle modifications, prophylactic therapy, and other supportive care strategies. This review summarizes the safety profiles of the currently approved TKIs and how they impact treatment selection in the first- and later-line settings of CML, particularly regarding patient comorbidities and concomitant medications. Additionally, strategies to manage AEs of special interest with TKIs are reviewed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".