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Record W3119071615 · doi:10.2217/fon-2020-1048

MOMENTUM: Momelotinib Vs Danazol in Patients with Myelofibrosis Previously Treated with JAKi Who Are Symptomatic and Anemic

2021· article· en· W3119071615 on OpenAlexaff
Srđan Verstovšek, Chih‐Cheng Chen, Miklós Egyed, Martin Ellis, María Laura Fox, Yeow Tee Goh, Vikas Gupta, Claire Harrison, Jean‐Jacques Kiladjian, Mihaela Lazaroiu, Adam J. Mead, Donal P. McLornan, Mary Frances McMullin, Stephen T. Oh, Andrew C. Perkins, Uwe Platzbecker, Christof Scheid, Alessandro M. Vannucchi, Sung‐Soo Yoon, Mark Kowalski, Ruben A. Mesa

Bibliographic record

VenueFuture Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsSierra Wireless (Canada)Princess Margaret Cancer Centre
FundersMedical Research CouncilCancer Research UK
KeywordsMyelofibrosisMedicineRuxolitinibAnemiaConstitutional symptomsInternal medicineBone marrow

Abstract

fetched live from OpenAlex

Hallmark features of myelofibrosis (MF) are cytopenias, constitutional symptoms and splenomegaly. Anemia and transfusion dependency are among the most important negative prognostic factors and are exacerbated by many JAK inhibitors (JAKi). Momelotinib (MMB) has been investigated in over 820 patients with MF and possesses a pharmacological and clinical profile differentiated from other JAKi by inhibition of JAK1, JAK2 and ACVR1. MMB is designed to address the complex drivers of iron-restricted anemia and chronic inflammation in MF and should improve constitutional symptoms and splenomegaly while maintaining or improving hemoglobin in JAKi-naive and previously JAKi-treated patients. The MOMENTUM Phase III study is designed to confirm and extend observations of safety and clinical activity of MMB.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.005
GPT teacher head0.230
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations38
Published2021
Admission routes1
Has abstractyes

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