Switching TKIs during CML therapy is frequent, mostly driven by intolerance, and does not affect survival: a prospective Quebec registry study
Bibliographic record
Abstract
Tyrosine kinase inhibitors (TKI) targeting the BCR::ABL1 fusion gene have dramatically improved the prognosis of patients with chronic myeloid leukemia (CML) to the point where most patients can enjoy a normal life expectancy [ 1 ]. Outside of clinical trials, the use of different TKIs in 1 L is largely influenced by drug accessibility, toxicity profile [ 2 ], and treatment-free remission (TFR) as the therapeutic goal [ 3 ]. However, there is a paucity of information on how therapeutic choices are made in the real-world (RW) setting, and on how resistance and tolerance drive switching compared to published clinical trials [ 4 ]. This study aimed to document treatment patterns and outcomes from a large provincial CML registry that includes 20 oncology centers, comprising both academic and community-based hospitals, reflecting a broad general treatment population.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".