Asciminib versus bosutinib following 2 or more prior therapies in chronic myeloid leukemia: a plain language summary of the ASCEMBL study
Bibliographic record
Abstract
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
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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; a candidate call from one teacher head, 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".