Long-term safety review of tyrosine kinase inhibitors in chronic myeloid leukemia - What to look for when treatment-free remission is not an option
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it