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Record W4405557300 · doi:10.1016/j.lrr.2024.100495

Targeting menin for precision therapy in high-risk acute myeloid leukemia

2024· review· en· W4405557300 on OpenAlexaff
Abdur Jamil, Zaheer Qureshi, Zain El‐amir, Gillian Kupakuwana-Suk, Hamzah Akram, Mohsin Ahmad, Eric Huselton

Bibliographic record

VenueLeukemia Research Reports · 2024
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsOttawa HospitalHamilton Health SciencesUniversity of Ottawa
Fundersnot available
KeywordsMedicineMyeloid leukemiaCancer researchOncologyInternal medicine

Abstract

fetched live from OpenAlex

Objective: This mini-review provides an overview of the current evidence for Revumenib, a first-in-class menin inhibitor, in treating AML with KMT2A rearrangements or NPM1 mutations. This therapy represents a promising advancement by selectively disrupting leukemogenic pathways. Summary: The clinical promise of Revumenib in genetically defined AML highlights its potential role in shaping the future treatment landscape. This mini-review underscores the need for ongoing trials to define optimal dosing, safety protocols, and combination therapies, with the ultimate goal of establishing Revumenib as a standard of care for high-risk AML subsets.

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)0.025
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0030.012
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.086
GPT teacher head0.430
Teacher spread0.345 · 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
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

Citations1
Published2024
Admission routes1
Has abstractyes

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