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Record W2754051730 · doi:10.1172/jci.insight.95679

Emerging therapies for acute myeloid leukemia: translating biology into the clinic

2017· review· en· W2754051730 on OpenAlex
Simon Kavanagh, Tracy Murphy, Arjun Law, Dana Yehudai, Jenny Ho, Aaron D. Schimmer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJCI Insight · 2017
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersCanadian Institutes of Health ResearchUniversity of TorontoLeukemia and Lymphoma Society
KeywordsMyeloid leukemiaDiseaseLeukemiaMyeloidMedicineMalignancyBiologyBioinformaticsImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Acute myeloid leukemia (AML) is an aggressive hematological malignancy with a poor outcome; overall survival is approximately 35% at two years and some subgroups have a less than 5% two-year survival. Recently, significant improvements have been made in our understanding of AML biology and genetics. These fundamental discoveries are now being translated into new therapies for this disease. This review will discuss recent advances in AML biology and the emerging treatments that are arising from biological studies. Specifically, we will consider new therapies that target molecular mutations in AML and dysregulated pathways such as apoptosis and mitochondrial metabolism. We will also discuss recent advances in immune and cellular therapy for AML.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.138
GPT teacher head0.459
Teacher spread0.322 · 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