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Record W4366603286 · doi:10.1080/10428194.2023.2196593

Ten years after ruxolitinib approval for myelofibrosis: a review of clinical efficacy

2023· review· en· W4366603286 on OpenAlexfundno aff
Naveen Pemmaraju, Prithviraj Bose, Raajit K. Rampal, Aaron T. Gerds, Angela Fleischman, Srđan Verstovšek

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2023
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsnot available
FundersGenentechServierAstellas PharmaGalectoSierra OncologyCTI BiopharmaIncytePfizerCareDxMorphoSysCelgeneBristol-Myers SquibbAstraZenecaConstellation PharmaceuticalsItalfarmacoMenarini GroupAstellas Pharma USGilead SciencesU.S. Department of Defense
KeywordsRuxolitinibMyelofibrosisMedicineMyeloproliferative neoplasmClinical trialJanus kinaseExpanded accessInternal medicineOncologyBone marrowCytokine

Abstract

fetched live from OpenAlex

Myelofibrosis (MF) is a chronic myeloproliferative neoplasm characterized by splenomegaly, abnormal cytokine expression, cytopenias, and progressive bone marrow fibrosis. The disease often manifests with burdensome symptoms and is associated with reduced survival. Ruxolitinib, an oral Janus kinase (JAK) 1 and JAK2 inhibitor, was the first agent approved for MF. As a first-in-class targeted treatment, ruxolitinib approval transformed the MF treatment approach and remains standard of care. In addition, targeted inhibition of JAK1/JAK2 signaling, a key molecular pathway underlying MF pathogenesis, and the large volume of literature evaluating ruxolitinib, have led to a better understanding of the disease and improved management in general. Here we review ruxolitinib efficacy in patients with MF in the 10 years following approval, including demonstration of clinical benefit in the phase 3 COMFORT-I/II trials, real-world evidence, translational studies, and expanded access data. Lastly, future directions for MF treatment are discussed, including ruxolitinib-based combination therapies.

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), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.000
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 head0.074
GPT teacher head0.395
Teacher spread0.320 · 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 designOther design
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

Citations46
Published2023
Admission routes1
Has abstractyes

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