<i>BCR–ABL1</i> transcript doubling time as a predictor for treatment‐free remission failure after imatinib discontinuation in chronic myeloid leukaemia in chronic phase
Bibliographic record
Abstract
The doubling time (DT) of the BCR-ABL1 quantitative polymerase chain reaction (qPCR) transcript level reflects the re-growing fraction of leukaemic cells after discontinuation of tyrosine kinase inhibitor (TKI). The present study analyzed monthly DT within six months after imatinib discontinuation in 131 patients. Monthly DT was calculated as x = ln(2)/K, where x is the DT and K is the fold BCR-ABL1 change from the previous value divided by the number of days between each measurement. The optimal DT value was determined as 12·75 days at two months using a recursive partitioning method. The patients were stratified into three groups: the high-risk group (DT<12·75 days but >0, with rapidly proliferating chronic myeloid leukaemia (CML) cells; n = 26) showed the lowest molecular relapse-free survival (mRFS) of 7·7% at 12 months, compared to 53·6% in the intermediate-risk group (DT≥12·75 days, with slowly proliferating CML cells; n = 16) or 90·0% in the low-risk group (DT≤0, i.e., without proliferating CML cells; n = 71; P < 0·001). Monthly assessment of DT helps identify high-risk patients for treatment-free remission failure with an imminent risk of molecular recurrence, and to define low-risk patients who can be spared the frequent monitoring of monthly molecular tests.
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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 | 0.002 |
| 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) | 0.000 | 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".