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Record W4390817926 · doi:10.1016/j.clml.2024.01.005

A Review of the Therapeutic Role of Bosutinib in Chronic Myeloid Leukemia

2024· review· en· W4390817926 on OpenAlexaff
Hagop M. Kantarjian, Elias Jabbour, Jeffrey H. Lipton, Fausto Castagnetti, Tim H. Brümmendorf

Bibliographic record

VenueClinical Lymphoma Myeloma & Leukemia · 2024
Typereview
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer Centre
FundersPfizer
KeywordsBosutinibMedicineDasatinibMyeloid leukemiaAdverse effectOncologyNilotinibPopulationInternal medicineTolerabilityImatinib

Abstract

fetched live from OpenAlex

The development of the BCR::ABL1 tyrosine kinase inhibitors (TKIs) has transformed Philadelphia chromosome (Ph)-positive chronic myeloid leukemia (CML) from a fatal disease to an often-indolent illness that, when managed effectively, can restore a life expectancy close to that of the normal population. Bosutinib is a second-generation TKI approved for adults with Ph-positive CML in chronic phase, accelerated phase, or blast phase that is resistant or intolerant to prior therapy, and for newly diagnosed Ph-positive chronic phase CML. This review details the efficacy of bosutinib for the treatment of CML in the first- and second-line settings, as well as in third- and later-line settings for high-risk patients resistant or intolerant to at least 2 TKIs. It also outlines bosutinib studies that provide evidence for dose-optimization strategies that can be used to improve efficacy and effectively manage adverse events. The studies that provide evidence for specific patient populations benefiting particularly from bosutinib dose-optimization strategies are also discussed. The well-established, long-term side-effect profile and the potential to make dose adjustments with bosutinib make it an appropriate treatment option for patients with CML. Bosutinib has demonstrated a positive impact on health-related quality of life and an important role in the long-term treatment of patients with CML.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.785
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.006
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.054
GPT teacher head0.387
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2024
Admission routes1
Has abstractyes

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