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Record W4406227864 · doi:10.1080/14796694.2024.2439178

Asciminib versus bosutinib following 2 or more prior therapies in chronic myeloid leukemia: a plain language summary of the ASCEMBL study

2025· article· en· W4406227864 on OpenAlexaff
Natasha Szuber, Lisa Machado, Dennis Dong Hwan Kim

Bibliographic record

VenueFuture Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health NetworkUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsBosutinibMedicineMyeloid leukemiaOncologySide effect (computer science)Internal medicineTyrosine-kinase inhibitorDrugImatinibPharmacologyNilotinibCancer

Abstract

fetched live from OpenAlex

Plain Language SummaryWhat is this summary about?This is a summary of a publication about the ASCEMBL study, which was published in Blood in November of 2021. Overall, researchers wanted to learn how well asciminib works compared to bosutinib, and how safe it is.The study included 233 adults with chronic myeloid leukemia (CML). All study participants had been previously exposed to 2 or more courses of treatment with tyrosine kinase inhibitors (TKI), but either did not respond to the treatment (referred to as being 'resistant') or needed to stop taking the medication due to side effects. Researchers wanted to know whether a new type of drug, called asciminib, could better improve the response of CML compared to standard therapy with a TKI called bosutinib. They also wanted to look at the side effects caused by asciminib compared to the ones caused by bosutinib. This is a summary of the published results at 24 weeks.What were the results?Researchers found that the improvement rate related to the patients’ treatment was higher in participants receiving asciminib. They looked at the chance of achieving major molecular response(MMR). After 24 weeks of therapy, MMR was higher in participants receiving asciminib (25.5%) compared to bosutinib (13.2%).The chance of achieving MMR was 12.2% higher in those treated with asciminib versus bosutinib. Furthermore, the proportion of patients who had to stop taking the drug due to side effects was much lower in those receiving asciminib (5.8%) compared to those taking bosutinib (21.1%).What are the key takeaways?Patients with chronic phase CML having previously been treated with 2 or more TKI treatments had greater benefit from asciminib compared to bosutinib. Asciminib was superior in terms of disease control, while also provoking fewer side effects. Asciminib could therefore be a treatment for people with CML who have not done well on other TKI treatments and could change the way CML is cared for.This is an abstract of the Plain Language Summary of Publication article.View the full Plain Language Summary PDF of this article to read the full-text

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.343
Teacher spread0.326 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes1
Has abstractyes

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