Switch to nilotinib versus continued imatinib in patients (pts) with chronic myeloid leukemia in chronic phase (CML-CP) with detectable BCR-ABL after 2 or more years on imatinib: ENESTcmr 12-month (mo) follow-up.
Bibliographic record
Abstract
6505^ Background: Nilotinib induced significantly faster and deeper molecular responses vs imatinib in the ENESTnd trial. Achieving these deeper molecular responses may increase patient eligibility for future TKI discontinuation studies. Methods: CML-CP pts (N = 207) who achieved a complete cytogenetic response but were still BCR-ABL positive by RQ-PCR after ≥ 24 mo on imatinib were randomized 1:1 to receive nilotinib 400 mg BID (n = 104) or to continue their imatinib dose (400 or 600 mg QD [n = 103]). The primary endpoint was confirmed CMR (undetectable BCR-ABL by RQ-PCR with a sample sensitivity of ≥ 4.5 logs in 2 consecutive samples). Other endpoints included molecular responses (MMR ≤ 0.1%IS, MR4 ≤ 0.01%IS, and MR4.5 ≤ 0.0032%IS) and BCR-ABL ratio over time. Results: Rate of confirmed CMR was higher in the nilotinib arm vs imatinib by 12 mo (12.5% vs 5.8%) (Table). Rate of CMR (undetectable BCR-ABL in at least 1 sample) by 12 mo was significantly higher on nilotinib vs imatinib (23.1% vs 10.7%; P = .02). Rates of MMR, MR4, MR4.5, and CMR were also superior in pts switched to nilotinib, and these pts had significantly shorter times to achieve these responses. Imatinib-treated pts had minimal evidence of improvement in molecular response vs a median 0.5-log reduction in BCR-ABL by 12 mo for the nilotinib cohort. With 12-mo follow-up, 84% of pts remained on nilotinib and 96% on imatinib. The nilotinib safety profile was consistent with prior studies. Both drugs were well tolerated. Conclusions: Twice as many pts achieved deeper molecular responses after switching to nilotinib vs staying on imatinib. [Table: see text]
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".