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Record W2990454217 · doi:10.1002/ajh.25688

Chronic neutrophilic leukemia: 2020 update on diagnosis, molecular genetics, prognosis, and management

2019· review· en· W2990454217 on OpenAlexaff
Natasha Szuber, Michelle A. Elliott, Ayalew Tefferi

Bibliographic record

VenueAmerican Journal of Hematology · 2019
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsLeukocytosisMonocytosisMedicineMyeloproliferative neoplasmHepatosplenomegalyDiseaseChronic myelomonocytic leukemiaImmunologyBone marrowBioinformaticsCancer researchOncologyMyelodysplastic syndromesInternal medicineMyelofibrosisBiology

Abstract

fetched live from OpenAlex

DISEASE OVERVIEW: Chronic neutrophilic leukemia (CNL) is a rare, often aggressive myeloproliferative neoplasm (MPN) defined by persistent mature neutrophilic leukocytosis, bone marrow granulocyte hyperplasia, and frequent hepatosplenomegaly. The seminal discovery of oncogenic driver mutations in colony-stimulating factor 3 receptor (CSF3R) in the majority of patients with CNL in 2013 anchored a new scientific framework, deepening our understanding of its molecular pathogenesis, providing a diagnostic biomarker, and rationalizing the use of pharmacological targeting. DIAGNOSTIC CRITERIA: /L comprising >80% neutrophils with <10% circulating precursors and rare blasts, and absence of dysplasia or monocytosis, while not fulfilling criteria for other MPN. DISEASE UPDATES: Increasingly comprehensive genetic profiling of CNL has disclosed a complex genomic landscape and additional prognostically relevant mutational combinations. Though prognostic determination and therapeutic decision-making remain challenging, emerging data on prognostic markers and the use of newer therapeutic agents, such as JAK inhibitors, are helping to define state-of-the-art management in CNL.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.320
Teacher spread0.301 · 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 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

Citations44
Published2019
Admission routes1
Has abstractyes

Explore more

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