A randomized trial of dasatinib 100 mg versus imatinib 400 mg in newly diagnosed chronic-phase chronic myeloid leukemia
Bibliographic record
Abstract
Tyrosine kinase inhibitor therapy with imatinib (IM), dasatinib (DAS), or nilotinib is very effective in chronic-phase chronic myeloid leukemia. Two hundred fifty-three patients with newly diagnosed chronic-phase chronic myeloid leukemia were randomized to IM 400 mg/day or DAS 100 mg/day. The proportion of patients achieving a complete cytogenetic remission rate was superior with DAS (84% vs 69%), as was the 12-month molecular response by the proportions of patients achieving > 3-log, > 4-log, and > 4.5-log reduction in BCR-ABL transcript levels. Overall and progression-free survival was similar in the 2 arms. Among patients who achieved hematologic CR, 3-year relapse-free survival was 91% with DAS and 88% with IM 400 mg. Grade 3 and 4 toxicities were most commonly hematologic, including thrombocytopenia in 18% and 8% of DAS and IM patients, respectively. DAS induced more complete cytogenetic response and deeper molecular responses after 12 months, compared with IM 400 mg, and with a median follow-up of 3.0 years there have been very few deaths, relapses, or progressions in the 2 arms. In summary, DAS compared with IM appeared to have more short-term cytogenetic and molecular response, more hematologic toxicity, and similar overall survival. This trial is registered at www.clinicaltrials.gov as NCT00070499.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".