Treatment‐free remission with first‐ and second‐generation tyrosine kinase inhibitors
Bibliographic record
Abstract
Chronic myeloid leukemia (CML) has become a chronic disease, for which the chronic phase is manageable with tyrosine kinase inhibitor (TKI) therapy. Patients with optimal responses to TKIs have achieved long-term survival, and treatment-free remission (TFR) has since become an additional treatment goal in CML. In this review, we discuss important factors to consider prior to stopping treatment. In addition, published and presented data with the first-generation TKI imatinib, as well as current clinical trials evaluating TFR with the second-generation TKIs dasatinib and nilotinib, are examined. Results obtained outside of clinical trials have been included as well. Because successful TKI discontinuation depends upon accurate BCR-ABL1 monitoring, emerging technologies are also discussed. Clinical data obtained to date indicate that for many patients who achieve deep molecular response (DMR) on TKI therapy, TFR is a safe treatment goal, and, if the response is lost, patients can expect to regain their responses immediately upon reinitiation of TKI. It is also clear that there remains much room for improvement to make TFR a successful reality for most patients. Data from ongoing trials should help refine decisions as to which patients are the best candidates to attempt TKI discontinuation with safe monitoring in place.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".