Ten years after ruxolitinib approval for myelofibrosis: a review of clinical efficacy
Bibliographic record
Abstract
Myelofibrosis (MF) is a chronic myeloproliferative neoplasm characterized by splenomegaly, abnormal cytokine expression, cytopenias, and progressive bone marrow fibrosis. The disease often manifests with burdensome symptoms and is associated with reduced survival. Ruxolitinib, an oral Janus kinase (JAK) 1 and JAK2 inhibitor, was the first agent approved for MF. As a first-in-class targeted treatment, ruxolitinib approval transformed the MF treatment approach and remains standard of care. In addition, targeted inhibition of JAK1/JAK2 signaling, a key molecular pathway underlying MF pathogenesis, and the large volume of literature evaluating ruxolitinib, have led to a better understanding of the disease and improved management in general. Here we review ruxolitinib efficacy in patients with MF in the 10 years following approval, including demonstration of clinical benefit in the phase 3 COMFORT-I/II trials, real-world evidence, translational studies, and expanded access data. Lastly, future directions for MF treatment are discussed, including ruxolitinib-based combination therapies.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".