Assessment of Clinical Outcome of Patients With Primary Myelofibrosis Treated With Ruxolitinib in the Real-World Practice
Bibliographic record
Abstract
BACKGROUND: Primary myelofibrosis (PMF) is a disease that characterized by bone marrow fibrosis which may result sometime in cytopenias and extramedullary hematopoiesis causing massive splenomegaly. Ruxolitinib (Rux) therapy that targeted Janus Kinase-2 receptor and approved for treatment of primary myelofibrosis. OBJECTIVE: evaluate the clinical outcome of patients with primary myelofibrosis receiving Ruxolitinib treatment as a compassionate use program and compare this treatment with the best available treatment (BAT). METHODS: This is a retrospective case series conducted at the national center of hematology /Mustansiriyah University. The enrollment of patients started in May 2014 and ended in May 2017. There were 22 patients diagnosed with PMF (7 on the Ruxolitinib arm and 15 on best available treatment arm). The treatment response was evaluated according to consensus criteria of IWG-MRT 2013 in primary myelofibrosis. RESULTS: In this study 3 out of 7 patients on Ruxolitinib arm showed reduction in spleen size and reduction in anemia. In addition to that 4 patients showed clinical response specifically in spleen size (with 65% reduction from baseline during the first three months of treatment). CONCLUSION: Rux is effective and safe to treating primary myelofibrosis with symptomatic splenomegaly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".