Patterns of Ruxolitinib Therapy Failure and Its Management in Myelofibrosis: Perspectives of the Canadian Myeloproliferative Neoplasm Group
Bibliographic record
Abstract
Ruxolitinib improves splenomegaly and other disease-related symptoms in patients with myelofibrosis, but over time, many patients lose this benefit. It is difficult to determine whether this is due to resistance or intolerance to the drug; thus, we have used the more inclusive term of ruxolitinib failure. The survival of patients with myelofibrosis after ruxolitinib failure is poor but varies significantly by the pattern of the failure, underlining the need for a clinically appropriate classification. In this review, we propose diagnostic guidance for early recognition of the pattern of ruxolitinib failure and we recommend treatment options. The most frequent patterns of ruxolitinib failure are loss or failure to obtain a significant reduction in splenomegaly or symptom response, and the development or persistence of clinically significant cytopenias. Ruxolitinib dose modification and other ancillary therapies are sometimes helpful, and splenectomy is a palliative option in selected cases. Stem-cell transplantation is the only curative option for these patterns of failure, but its restricted applicability due to toxicity highlights the importance of ongoing clinical trials in this area. Recent approval of fedratinib by the US Food and Drug Administration provides an alternative option for patients with suboptimal or loss of spleen response. The transformation of myelofibrosis to accelerated or blast phase is an infrequent form of failure with an extremely poor prognosis, whereby patients who are ineligible for transplantation have limited treatment options.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".