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Record W2295530774 · doi:10.3324/haematol.2015.135418

Activity of the Janus kinase inhibitor ruxolitinib in chronic lymphocytic leukemia: results of a phase II trial

2016· letter· en· W2295530774 on OpenAlexafffundabout
David Spaner, G Wang, Lindsay McCaw, Y. Li, Patricia Disperati, Mary‐Anne Cussen, Yonghong Shi

Bibliographic record

VenueHaematologica · 2016
Typeletter
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsToronto East General HospitalSunnybrook HospitalUniversity of TorontoSunnybrook Health Science Centre
FundersLeukemia and Lymphoma Society of CanadaUniversity of TorontoLeukemia and Lymphoma Society
KeywordsRuxolitinibIdelalisibJanus kinaseMyelofibrosisVenetoclaxMedicineChronic lymphocytic leukemiaIbrutinibOncologySTAT proteinInternal medicineJanus kinase 1Clinical trialstatClinical endpointCancer researchTolerabilitySTAT3LeukemiaSignal transductionChemistryAdverse effectReceptorBone marrow

Abstract

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B cell receptor (BCR)-signaling inhibitors such as Ibrutinib and Idelalisib do not cure CLL, suggesting the importance of other pathways.1 Cytokines are also important in CLL biology and mediate transcription of oncogenic genes such as miR-17 by activating signal transducer and activator of transcription (STAT) proteins including STAT5A/B and STAT3 through Janus kinases (JAKs).2–4 We hypothesized that Ruxolitinib, a selective JAK1/2 inhibitor licensed for myelofibrosis,5 might have unrecognized activity in CLL and designed a single center phase II trial to evaluate it in patients considered unfit for FCR on the basis of age and comorbidities.6 The demonstrated safety of Ruxolitinib in myelofibrosis was felt to make it ethical to use as first-line therapy for elderly CLL patients. The primary endpoint was overall response rate (ORR) assessed after 7 treatment cycles.7 Secondary endpoints were safety and tolerability.7 The study was approved by the Sunnybrook REB and Health Canada and registered with ClinicalTrials.gov, {type:clinical-trial,attrs:{text:NCT02015208,term_id:NCT02015208}}NCT02015208.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
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 head0.047
GPT teacher head0.333
Teacher spread0.287 · 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
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

Citations34
Published2016
Admission routes3
Has abstractyes

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