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Record W4408957812 · doi:10.1038/s41408-025-01263-3

Venetoclax and azacitidine in untreated patients with therapy-related acute myeloid leukemia, antecedent myelodysplastic syndromes or chronic myelomonocytic leukemia

2025· letter· en· W4408957812 on OpenAlexaff
Vinod Pullarkat, Keith W. Pratz, Hartmut Döhner, Christian Récher, Michael J. Thirman, Courtney D. DiNardo, Pierre Fenaux, Andre C. Schuh, Andrew H. Wei, Arnaud Pigneux, Jun‐Ho Jang, Gunnar Juliusson, Yasushi Miyazaki, Dominik Selleslag, Martha Arellano, Chenglong Liu, Jean Ridgeway, Jalaja Potluri, Jovita Schuler, Marina Konopleva

Bibliographic record

VenueBlood Cancer Journal · 2025
Typeletter
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
FundersGenentechAbbVie
KeywordsAzacitidineChronic myelomonocytic leukemiaMyelodysplastic syndromesMedicineMyeloid leukemiaVenetoclaxLeukemiaAntecedent (behavioral psychology)Internal medicineMyeloidOncologyImmunologyChronic lymphocytic leukemiaBone marrowPsychologyPsychotherapistBiology

Abstract

fetched live from OpenAlex

Secondary acute myeloid leukemia (sAML), a subset of AML, may arise from antecedent hematologic disorders (antecedent myelodysplastic syndrome or chronic myelomonocytic leukemia [A-MDS/CMML]) or complication of prior cytotoxic chemotherapy or radiation therapy (therapy-related AML [tAML]) [ 1 ]. It comprises about 25% to 35% of AML cases, occurring more frequently with age [ 2 ]. Rising incidence of sAML is potentially related to increased survival from prior malignancies, greater use of chemotherapy, and improved reporting of myeloid malignancies [ 2 ]. sAML is frequently associated with adverse genetics, including TP53 mutations, which are associated with poor outcomes in myeloid malignancies [ 2 , 3 ]. Compared with primary AML, sAML is associated with unfavorable outcomes regardless of age, posing unique clinical challenges [ 4 , 5 ].

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.274
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations7
Published2025
Admission routes1
Has abstractyes

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