Optimal duration of imatinib treatment/deep molecular response for treatment‐free remission after imatinib discontinuation from a Canadian tyrosine kinase inhibitor discontinuation trial
Bibliographic record
Abstract
Although total duration of tyrosine kinase inhibitor (TKI) therapy and of molecular response at 4 log reduction or deeper (MR4) correlates with treatment-free remission (TFR) success after TKI discontinuation, the optimal cut-off values of the duration remain unresolved. Thus, 131 patients were enrolled into the Canadian TKI discontinuation study. The molecular relapse-free survival (mRFS) was defined from imatinib discontinuation till molecular recurrence, that is, major molecular response (MMR) loss and/or MR4 loss. We evaluated mRFS at 12 months after imatinib discontinuation, analyzed it according to the imatinib treatment duration and MR4 duration, and calculated P value, positive (PPV) and negative predictive value (NPV) in the yearly cut-off period of time. The shortest cut-off was sought that met the joint criteria of a P value ≤ 0·05, PPV ≥ 60% and NPV ≥ 60%. We propose six years as the shortest imatinib duration cut-off with a P value 0·01, PPV 68% and NPV 62%: The patients treated with imatinib duration ≥ 6 years showed a superior mRFS rate (61·8%) compared to those with less treatment (36·0%). Also, 4·5 years MR4 duration as the shortest cut-off with a P value 0·003, PPV 63% and NPV 61%: those with MR4 duration ≥ 4·5 years showed a higher mRFS rate (64·2%) than those with a shorter MR4 duration (41·9%).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".