Chronic neutrophilic leukemia: 2022 update on diagnosis, genomic landscape, prognosis, and management
Bibliographic record
Abstract
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 2013 seminal discovery of oncogenic driver mutations in colony-stimulating factor 3 receptor (CSF3R) in the majority of patients with CNL not only established its molecular pathogenesis but provided a diagnostic biomarker and rationale for pharmacological targeting. DIAGNOSIS: /L comprising >80% neutrophils with <10% circulating precursors and rare blasts, and absence of dysplasia or monocytosis, while not fulfilling criteria for other MPN. MANAGEMENT: There is currently no standard of care for management of CNL, due in large part to the rarity of disease and dearth of formal clinical trials. Most commonly used therapeutic agents include conventional oral chemotherapy (e.g., hydroxyurea), interferon, and Janus kinase (JAK) inhibitors, while hematopoietic stem cell transplant remains the only potentially curative modality. DISEASE UPDATES: Increasingly comprehensive genetic profiling in CNL, including new data on clonal evolution, has disclosed a complex genomic landscape with additional mutations and combinations thereof driving disease progression and drug resistance. Although accurate prognostic stratification and therapeutic decision-making remain challenging in CNL, emerging data on molecular biomarkers and the addition of newer agents, such as JAK inhibitors, to the therapeutic arsenal, are paving the way toward greater standardization and improvement of patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".