Survival outcomes and clinical benefit in patients with acute myeloid leukemia treated with glasdegib and low-dose cytarabine according to response to therapy
Bibliographic record
Abstract
BACKGROUND: The phase 2 BRIGHT AML 1003 trial evaluated efficacy and safety of glasdegib + low-dose cytarabine (LDAC) in patients with acute myeloid leukemia ineligible for intensive chemotherapy. The multicenter, open-label study randomized patients to receive glasdegib + LDAC (n = 78) or LDAC alone (n = 38). The rate of complete remission (CR) was 19.2% in the glasdegib + LDAC arm versus 2.6% in the LDAC arm (P = 0.015). METHODS: This post hoc analysis determines whether the clinical benefits of glasdegib are restricted to patients who achieve CR, or if they extend to those who do not achieve CR. RESULTS: In patients who did not achieve CR, the addition of glasdegib to LDAC improved overall survival (OS) versus LDAC alone (hazard ratio = 0.63 [95% confidence interval, 0.41-0.98]; P = 0.0182; median OS, 5.0 vs 4.1 months). Additionally, more patients receiving glasdegib + LDAC achieved durable recovery of absolute neutrophil count (≥ 1000/μl, 45.6% vs 35.5%), hemoglobin (≥ 9 g/dl, 54.4% vs 38.7%), and platelets (≥ 100,000/μl, 29.8% vs 9.7%). Transfusion independence was achieved by 15.0% and 2.9% of patients receiving glasdegib + LDAC and LDAC alone, respectively. CONCLUSIONS: Collectively, these data suggest that there are clinical benefits with glasdegib in the absence of CR. TRIAL REGISTRATION: ClinicalTrials.gov NCT01546038 (March 7, 2012).
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".