<i><scp>BCR</scp>‐<scp>ABL</scp>1</i> transcript at 3 months predicts long‐term outcomes following second generation tyrosine kinase inhibitor therapy in the patients with chronic myeloid leukaemia in chronic phase who failed Imatinib
Bibliographic record
Abstract
The BCR-ABL1 transcript level at 3 months can predict long-term outcomes following frontline therapy with Imatinib or Dasatinib in chronic myeloid leukaemia (CML) patients. However, data is lacking for second-generation tyrosine kinase inhibitor (2GTKI) therapy after Imatinib failure. A total of 112 patients with CML in chronic phase receiving 2GTKI after Imatinib failure were reviewed. Treatment outcomes including complete cytogenetic (CCyR), major molecular (MMR) and molecular response 4.5 (4.5 log reduction of BCR-ABL1 transcript level, MR(4.5) ), treatment failure, progression-free and overall survival (OS) were compared according to BCR-ABL1 transcript levels at 3 or 6 months, divided into <1%(IS) , 1-10%(IS) and ≥ 10%(IS) . BCR-ABL1 transcript level at 3 months showed better correlation with OS (P < 0.001) than that at 6 months (P = 0.147). Better OS was also observed in the patients achieving <1%(IS) (100%) and 1-10%(IS) (100%) than those with ≥ 10%(IS) at 3 months (70.6%, P < 0.001). Those with <1%(IS) showed the best CCyR, MMR and MR(4.5) rates; 1-10%(IS) , intermediate; and ≥ 10%(IS) , the lowest CCyR, MMR and MR(4.5) rates. The group with <1%(IS) at 3 months maintained significantly lower BCR-ABL1 transcript level compared to other two groups. In conclusion, the BCR-ABL1 transcript level at 3 months is the most relevant surrogate for outcomes following 2GTKI therapy after Imatinib failure.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".