Management of chronic myeloid leukemia in 2023 – common ground and common sense
Bibliographic record
Abstract
With the improving knowledge of CML and its management, the goals of therapy need to be revisited to ensure an optimal use of the BCR::ABL1 TKIs in the frontline and later-line therapy of CML. In the frontline therapy of CML in the chronic phase (CML-CP), imatinib and the three second-generation TKIs (bosutinib, dasatinib and nilotinib) are associated with comparable survival results. The second-generation TKIs may produce earlier deep molecular responses, hence reducing the time to reaching a treatment-free remission (TFR). The choice of the second-generation TKI versus imatinib in frontline therapy is based on the treatment aims (survival, TFR), the CML risk, the drug cost, and the toxicity profile with respect to the patient's comorbidities. The TKI dosing is more flexible than has been described in the registration trials, and dose adjustments can be considered both in the frontline and later-line settings (e.g., dasatinib 50 mg frontline therapy; dose adjusted schedules of bosutinib and ponatinib), as well as during an ongoing TKI therapy to manage toxicities, before considering changing the TKI. In patients who are not candidates for TFR, BCR::ABL1 (International Scale) transcripts levels <1% are acceptable, result in virtually similar survival as with deeper molecular remissions, and need not warrant a change of TKI. For patients with true resistance to second-generation TKIs or with the T315I gatekeeper mutation, the third-generation TKIs are preferred. Ponatinib should be considered first because of the cumulative experience and results in the CML subsets, including in T315I-mutated CML. A response-based dosing of ponatinib is safe and leads to high TKI compliance. Asciminib is a third-generation TKI with possibly a better toxicity profile, but lesser activity in T315I-mutated CML. Olverembatinib is another potent third-generation TKI with early promising results.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".