Myelofibrosis: Treatment Options After Ruxolitinib Failure
Bibliographic record
Abstract
While allogeneic hematopoietic stem cell transplantation remains the only curative therapy for patients with myelofibrosis, its applicability is limited both by the high morbidity and mortality associated with the procedure and by the fact that only a minority of patients are eligible due to age or comorbidities. Ruxolitinib, a JAK1/JAK2 inhibitor, is the standard first-line therapy for intermediate- and high-risk MF, offering symptom relief and splenic volume reduction but lacking a clear survival benefit. Its use may be limited by hematologic toxicities, increased infection risk, and an inability to prevent disease progression. Ruxolitinib failure remains a significant clinical challenge, with resistance mechanisms not fully elucidated. The approval of other JAK inhibitors-fedratinib, pacritinib, and momelotinib-has expanded treatment options, particularly for patients with cytopenias or transfusion dependence. Moreover, many other targeted agents are in development in clinical trials, as monotherapy or in combination with ruxolitinib. This review provides an update on the use of JAK inhibitors and novel agents, with a focus on treatment options for ruxolitinib-resistant or refractory patients. As therapeutic strategies evolve, optimizing treatment sequencing and incorporating next-generation sequencing will be critical for improving patient outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".