The comparative effectiveness of glasdegib in combination with low-dose cytarabine versus azacitidine by bone marrow blasts counts among patients with newly-diagnosed acute myeloid leukemia who are ineligible for intensive chemotherapy.
Bibliographic record
Abstract
e19512 Background: Acute myeloid leukemia (AML) is an orphan disease with one of the lowest five-year survival rates among myeloid malignancies. Recently, a randomized, open label study among previously untreated, chemotherapy-ineligible AML patients demonstrated improved overall survival (OS) among patients treated with glasdegib (GLAS) + LDAC compared with LDAC alone. Two trials of AZA vs. conventional care regimens report data by bone marrow blast (BMB) counts: one with 20-30% and the other with >30%. In the absence of head-to-head comparisons, this study aims to perform the indirect treatment comparison between GLAS+LDAC and AZA by BMB counts. Methods: As there were potential imbalances between the GLAS and AZA trials and within the AZA trial arms in the baseline characteristics (e.g. poor cytogenetics% and de novo%), simulated treatment comparisons (STCs) for GLAS+LDAC vs. LDAC were performed to derive robust estimation by adjusting for the imbalances in the baseline effect modifiers. Afterwards, the classical network meta-analysis (NMA) was conducted. To derive the hazard ratio (HR) of GLAS+LDAC vs. AZA, three NMAs were conducted in each BMB group. Each NMA used a different HR of GLAS+LDAC vs. LDAC: 1) an unadjusted HR (classical NMA), 2) an STC adjusted HR adjusting for potential imbalances between the trials, and 3) an STC adjusted HR additionally accounting for potential imbalances between arms within the AZA trial. Results: In the 20-30% BMB group (N = 30), the OS HRs of GLAS+LDAC vs. AZA resulting from the three respective NMAs were as follows: 1) 0.46 [95% confidence interval: 0.10-2.14], 2) 0.31 [0.06-1.69], and 3) 0.36 [0.06-2.15]. In the > 30% BMB group (N = 80), the HRs were 1) 0.69 [0.39-1.20], 2) 0.48 [0.23-0.97], and 3) 0.48 [0.24-1.00]. All the HRs suggest that patients with GLAS+LDAC have a survival advantage over patients with AZA. Conclusions: Both the classical NMAs and the NMAs based on the STC adjusted HRs correcting for the potential imbalances at baseline suggest that GLAS+LDAC may be preferred over AZA as a treatment option for previously untreated chemotherapy-ineligible AML patients regardless of BMB counts. [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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".