Glasdegib (GLAS) plus low-dose cytarabine (LDAC) in AML or MDS: BRIGHT AML 1003 final report and four-year overall survival (OS) follow-up.
Bibliographic record
Abstract
7509 Background: In newly diagnosed AML or high-risk MDS, primary analysis of the randomized phase 2 BRIGHT AML 1003 trial (data cutoff Jan 2017) showed superior OS for GLAS+LDAC vs LDAC alone. The trial then continued to predefined completion 4 years from randomization of all patients (pts), reached in Mar 2019 and presented herein. Methods: Pts with newly diagnosed AML or high-risk MDS and unsuitable for intensive chemotherapy were randomized 2:1 to GLAS+LDAC (n=88) or LDAC alone (n=44). For these groups, median (range) treatment duration was 83 (3-1575) and 47 (6-239) days; median follow-up for survival 47.6 and 48.1 months; 4 (4.5%) and 1 pt (2.3%) completed ≥4 years’ follow-up. Results: Consistent with the primary findings (OS HR 0.51; 80% CI 0.39, 0.67 p=0.0004), GLAS+LDAC prolonged OS vs LDAC alone (table). Results were consistent in AML pts and cytogenetic risk/disease history subgroups (table), as were analyses by pt characteristics and baseline risk factors (not shown). Survival probability was 39.5% vs 9.5% at 1 year and 18.0% vs 2.4% at 2 years. GLAS+LDAC induced higher complete remission (CR) rates overall (16/88 vs 1/44; RR 8.12, 95% CI 1.05, 62.78, p=0.010) and across subgroups. Notably, fewer pts discontinued GLAS+LDAC due to AEs (38.1% and 46.3%) and there was no increased sepsis or bleeding vs LDAC alone despite longer glasdegib treatment. With GLAS+LDAC, SMO-inhibitor-associated AEs included dysgeusia (25.0%), muscle spasms (22.6%) and alopecia (10.7%), with only 1 pt discontinuing due to dysgeusia. Conclusions: Consistent with primary analyses, GLAS+LDAC continued to have an acceptable safety profile and improved OS vs LDAC alone. HRs were consistent across cytogenetic risk subgroups and support use of GLAS+LDAC in de novo and secondary AML. Clinical trial information: NCT01546038 . [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 | 0.003 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".