Nilotinib dose‐optimization in newly diagnosed chronic myeloid leukaemia in chronic phase: final results from <scp>ENEST</scp>xtnd
Bibliographic record
Abstract
The Evaluating Nilotinib Efficacy and Safety in Clinical Trials-Extending Molecular Responses (ENESTxtnd) study was conducted to evaluate the kinetics of molecular response to nilotinib in patients with newly diagnosed chronic myeloid leukaemia in chronic phase and the impact of novel dose-optimization strategies on patient outcomes. The ENESTxtnd protocol allowed nilotinib dose escalation (from 300 to 400 mg twice daily) in the case of suboptimal response or treatment failure as well as dose re-escalation for patients with nilotinib dose reductions due to adverse events. Among 421 patients enrolled in ENESTxtnd, 70·8% (95% confidence interval, 66·2-75·1%) achieved major molecular response (BCR-ABL1 ≤ 0·1% on the International Scale) by 12 months (primary endpoint). By 24 months, 81·0% of patients achieved major molecular response, including 63·6% (56 of 88) of those with dose escalations for lack of efficacy and 74·3% (55 of 74) of those with dose reductions due to adverse events (including 43 of 54 patients with successful re-escalation). The safety profile of nilotinib was consistent with prior studies. The most common non-haematological adverse events were headache, rash, and nausea; cardiovascular events were reported in 4·5% of patients (grade 3/4, 3·1%). The study was registered at clinicaltrials.gov (NCT01254188).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".