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Record W4392108373 · doi:10.15173/m.v1i35.2205

Pacritinib: A novel therapeutic agent for treating myelofibrosis

2019· article· en· W4392108373 on OpenAlexvenueno aff
The Meducator, Kashyap Patel, Ruby Zheng

Bibliographic record

VenueThe Meducator · 2019
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMyelofibrosisMedicineInternal medicine

Abstract

fetched live from OpenAlex

Selective inhibitors are an important advancement in clinical medicine, providing a method to target proteins within signal transductionpathways. One such inhibitor, pacritinib, targets Janus Kinase 2 (JAK2) to treat patients with myelofibrosis (MF). In MF patients diagnosedwith a specific point mutation (JAK2V61F), the JAK/STAT pathway is over-activated, leading to an increase in cell proliferation rates.Pacritinib restores normal JAK/STAT activity by targeting aberrant activity of JAK2V61F mutant proteins and inhibiting cell proliferation inbone marrow tissue. In recent studies, pacritinib has shown better results at treating MF than the best available therapy. Though pacritinibis a potentially effective treatment option, it does not address all aspects of MF and should be supplemented with other treatments.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.033
GPT teacher head0.315
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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