Final report of <scp>TKI</scp> discontinuation trial with dasatinib for the second attempt of treatment‐free remission after failing the first attempt with imatinib: Treatment‐free Remission Accomplished by Dasatinib (<scp>TRAD</scp>) study
Bibliographic record
Abstract
Multiple studies have reported a significant treatment-free remission (TFR) rate of 50%-60% in patients with chronic myeloid leukaemia (CML) who discontinue tyrosine kinase inhibitor (TKI) therapy. However, the remaining half of these patients still require re-initiation of TKI therapy for leukaemia control. It remains unclear if TKI drugs should be switched for re-therapy in patients who failed the first TFR (TFR1) attempt. Our study attempted to determine whether dasatinib therapy after TFR1 failure post-imatinib discontinuation could improve the likelihood of TFR2. Of 59 patients who lost molecular response after imatinib discontinuation for TFR1, 55 patients (93.2%) were treated with dasatinib, of whom 49 (89.1%) regained MR4.5 or deeper response, with a median time of 1.85 months to achieve MR4.5. Dasatinib was discontinued in 35 patients for TFR2 attempt, of whom 26 patients (74.28%) lost MMR and 6 (17.14%) MR4. Risk factor analysis for the TFR2 after dasatinib discontinuation suggested three significant factors: (1) doubling time of BCR::ABL1 transcript following TFR1 attempt, (2) rapid regaining of molecular response following dasatinib therapy and (3) undetectable BCR::ABL1 transcript prior to TFR2 attempt. The present study showed that dasatinib does not increase the TFR2 rate in general, but a selected group of patients could benefit from this approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".