Momelotinib Usage Within Our Current Canadian Myelofibrosis Armamentarium
Bibliographic record
Abstract
Myelofibrosis (MF) can be categorized as primary MF (PMF), or secondary MF, which comprises post-polycythemia MF (PPV) and post-essential thrombocythemia (PET). Activating mutations in JAK2, CALR, or MPL are the main driver mutations resulting in abnormal signalling that promotes cell proliferation and survival, leading to secretion of inflammatory cytokines causing myeloproliferation, bone marrow fibrosis, and extramedullary hematopoiesis in MF. The current treatment landscape for MF consists of strategies to reduce spleen volume and improve MF-related symptoms with less effective results in improving cytopenias. Mainstay therapies have included hydroxyurea (HU) and Janus kinase inhibitors (JAKi), as well as curative allogeneic stem cell transplant (ASCT), though fewer patients are eligible for this treatment. Several JAKi have been approved in Canada for first-line treatment, including ruxolitinib, fedratinib, and most recently, momelotinib. Approximately 40% of patients with MF have anemia at diagnosis, and nearly 25% are red blood cell (RBC) transfusion-dependent (TD). Many patients with MF struggle with symptoms related to chronic anemia, and anemia often progresses with time, leading to transfusion dependence for many patients. Anemia of any severity negatively impacts MF survival and is highlighted as a negative prognostic factor among most validated MF scoring systems. Anemia results in increased patient fatigue and lower quality of life (QoL), which results in increased healthcare utilization. Severe anemia results in a 2-fold increased healthcare resource utilization compared to mild anemia. This review focuses on the current treatment approaches for MF, with particular focus on MF-related anemia and the targeted role of newer JAKi, such as momelotinib.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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.
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 source (direct Gemma or distilled Codex), 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".