Management of Chronic Myeloid Leukemia that is Intolerant or Resistant to Front-Line Treatment
Bibliographic record
Abstract
With advances in treatment for chronic myeloid leukemia (CML), the natural history of chronic phase (CP) CML has changed, with most individuals expected to live a normal life expectancy. The goal of therapy for most is to achieve a long-term deep molecular response (DMR) with the potential for medication discontinuation and treatment-free remission (TFR).1 Currently, six oral therapies have been approved for CP-CML in Canada: (1) imatinib, a first-generation tyrosine kinase inhibitor (TKI); (2) dasatinib, (3) nilotinib, and (4) bosutinib, the second-generation TKIs (2G-TKIs); (5) ponantinib, a third-generation TKI; and (6) asciminib, specifically targeting the ABL Myristoyl pocket (STAMP) inhibitor. Classically, treatment for CP-CML has consisted of front-line imatinib and switching to a 2G-TKI upon treatment resistance or intolerance. Increasingly, patients are being prescribed an upfront 2G-TKI with the goal of achieving quicker and deeper molecular remissions and a TFR.2 Challenges arise in CML when treatment with either two TKIs (imatinib + 2G-TKI) or one 2G-TKI fails, given the lack of evidence to inform clinical decision-making at this juncture. This paper aims to define TKI failure and help guide the selection of second-line treatment after failure of front-line therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".