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Record W4200625806 · doi:10.1182/hematology.2021000279

Novel therapies vs hematopoietic cell transplantation in myelofibrosis: who, when, how?

2021· review· en· W4200625806 on OpenAlexaff
James T. England, Vikas Gupta

Bibliographic record

VenueHematology · 2021
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMyelofibrosisMedicineTolerabilityTransplantationHematopoietic stem cell transplantationDiseaseClinical trialOncologyStem cellInternal medicineIntensive care medicineImmunologyBone marrowAdverse effectBiology

Abstract

fetched live from OpenAlex

Myelofibrosis is one of the classical Philadelphia chromosome-negative myeloproliferative neoplasms characterized by progressive marrow failure and chronic inflammation. Discovery of the JAK2 mutation paved the way for development of small molecular inhibitors and further facilitated the research in understanding of molecular biology of the disease. Development of novel medications and synergistic combinations with standard JAK inhibitor (JAKi) therapy may have the potential to improve depth and duration of disease control and symptomatic benefit, whereas advancements in allogeneic hematopoietic stem cell transplantation (HCT) have improved tolerability and donor availability, allowing for more patients to pursue this potentially curative therapy. The increase in options for medical therapy and changing risk profile of HCT is leading to increased complexity in counseling patients on choice of management strategy. In this case-based review, we summarize our approach to symptom-directed medical therapy, including the use of novel drugs and combination therapies currently under study in advanced clinical trials. We outline our recommendations for optimal timing of HCT, including risk-adapted selection for early HCT as opposed to delayed HCT after upfront JAKi therapy, as well as the use of pretransplant JAKi and alternative donor sources.

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 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 score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.045
GPT teacher head0.316
Teacher spread0.272 · 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.

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

Citations15
Published2021
Admission routes1
Has abstractyes

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