JAK inhibitor selection in challenging scenarios of myelofibrosis: a review
Bibliographic record
Abstract
Myelofibrosis is a progressive myeloproliferative neoplasm characterized by dysregulated Janus kinase (JAK)/signal transducer and activator of transcription signaling. Common clinical manifestations include constitutional symptoms, splenomegaly, and anemia, which can significantly impact quality of life and survival. Although hematopoietic stem cell transplant is the only curative treatment modality in myelofibrosis, JAK inhibitors have transformed management by providing symptom relief and reducing spleen size in many patients; newer JAK inhibitors also offer anemia-related benefits. Four JAK inhibitors - ruxolitinib, fedratinib, pacritinib, and momelotinib - are now available for the treatment of myelofibrosis, each with distinct profiles and safety considerations that may inform selection. However, head-to-head trial comparisons are limited, and real-world experience with most of these JAK inhibitors is only just emerging; therefore, first-line selection and optimal sequencing in particular patients can be challenging. This review summarizes the current data surrounding available JAK inhibitors for the treatment of patients with myelofibrosis and examines how individual patients' characteristics can help guide selection among them. To illustrate the diverse clinical factors and key considerations associated with JAK inhibitor selection in practice, we discuss these data in the context of four hypothetical cases representing possible real-world scenarios, offering treatment recommendations based on our collective expertise in the field. As the myelofibrosis therapeutic landscape continues to evolve, a thorough understanding of the strengths and limitations of each JAK inhibitor relative to a given patient's presentation will support individualized treatment decisions for optimal long-term outcomes.
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 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) | 0.000 | 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".