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Record W4415603472 · doi:10.1007/s00277-025-06643-0

Efficacy and safety of venetoclax plus azacitidine based regimens in the treatment of relapsed or refractory acute myeloid leukemia: a systematic review and meta-analysis

2025· review· en· W4415603472 on OpenAlexaboutno aff
Qinyi Cai, Jiayi Xiao, C. Weng, Huiling Chen

Bibliographic record

VenueAnnals of Hematology · 2025
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
FundersLanzhou University
KeywordsVenetoclaxAzacitidineVenNeutropeniaMyeloid leukemiaFebrile neutropeniaChemotherapyRefractory (planetary science)Adverse effect

Abstract

fetched live from OpenAlex

This meta-analysis aimed to evaluate the efficacy and safety of venetoclax plus azacitidine (VEN + AZA) regimens in patients with relapsed or refractory acute myeloid leukemia (R/R AML) and to explore the effects of different combination strategies, including chemotherapy and targeted agents, on clinical outcomes. A systematic search of PubMed, Web of Science, Embase, and Cochrane Library databases was performed up to February 2025. Studies that reported complete remission or complete remission with incomplete hematologic recovery (CR/CRi) were included. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) for Non-Randomized Controlled Trials (NRCTs). Pooled estimates were calculated using random-effects models, and subgroup analyses were performed. The CR/CRi rate for VEN + AZA-based regimens was 43% (95% CI: 33-53%), with substantial heterogeneity (I²=89.20%). Subgroup analysis indicated higher CR/CRi rates for VEN + AZA combined with chemotherapy (68%, 95% CI: 62-73%) compared to VEN + AZA alone (38%, 95% CI: 28-47%) or VEN + AZA with targeted agents (28%, 95% CI: 18-40%). The most common grade ≥ 3 adverse events were neutropenia (89%) and thrombocytopenia (82%). VEN + AZA combined with chemotherapy significantly improved CR/CRi rates in R/R AML compared to VEN + AZA alone or with targeted agents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0200.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.133
GPT teacher head0.421
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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