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Record W4225268854 · doi:10.1002/cncr.34205

Defining disease modification in myelofibrosis in the era of targeted therapy

2022· review· en· W4225268854 on OpenAlexaff
Naveen Pemmaraju, Srđan Verstovšek, Ruben A. Mesa, Vikas Gupta, Jacqueline S. Garcia, Joseph M. Scandura, Stephen T. Oh, Francesco Passamonti, Konstanze Döhner, Adam J. Mead

Bibliographic record

VenueCancer · 2022
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersMedical Research CouncilCancer Research UKRosetrees TrustAbbVie
KeywordsMedicineMyelofibrosisClinical trialDiseaseIntensive care medicineDrug developmentBone marrowDrugInternal medicinePharmacology

Abstract

fetched live from OpenAlex

The development of targeted therapies for the treatment of myelofibrosis highlights a unique issue in a field that has historically relied on symptom relief, rather than survival benefit or modification of disease course, as key response criteria. There is, therefore, a need to understand what constitutes disease modification of myelofibrosis to advance appropriate drug development and therapeutic pathways. Here, the authors discuss recent clinical trial data of agents in development and dissect the potential for novel end points to act as disease modifying parameters. Using the rationale garnered from latest clinical and scientific evidence, the authors propose a definition of disease modification in myelofibrosis. With improved overall survival a critical outcome, alongside the normalization of hematopoiesis and improvement in bone marrow fibrosis, there will be an increasing need for surrogate measures of survival for use in the early stages of trials. As such, the design of future clinical trials will require re-evaluation and updating to incorporate informative parameters and end points with standardized definitions and methodologies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.095
GPT teacher head0.390
Teacher spread0.294 · 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 teacher head, not a consensus.

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

Citations58
Published2022
Admission routes1
Has abstractyes

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