Primary analysis of JUMP, a phase 3b, expanded‐access study evaluating the safety and efficacy of ruxolitinib in patients with myelofibrosis, including those with low platelet counts
Bibliographic record
Abstract
Summary Ruxolitinib is a potent Janus kinase (JAK) 1/JAK2 inhibitor approved for the treatment of myelofibrosis (MF). Ruxolitinib was assessed in JUMP, a large ( N = 2233), phase 3b, expanded‐access study in MF in countries without access to ruxolitinib outside a clinical trial, which included patients with low platelet counts (<100 × 10 9 /l) and patients without splenomegaly – populations that have not been extensively studied. The most common adverse events (AEs) were anaemia and thrombocytopenia, but they rarely led to discontinuation (overall, 5·4%; low‐platelet cohort, 12·3%). As expected, rates of worsening thrombocytopenia were higher in the low‐platelet cohort (all grades, 73·2% vs. 53·5% overall); rates of anaemia were similar (all grades, 52·9% vs. 59·5%). Non‐haematologic AEs, including infections, were mainly grade 1/2. Overall, ruxolitinib led to meaningful reductions in spleen length and symptoms, including in patients with low platelet counts, and symptom improvements in patients without splenomegaly. In this trial, the largest study of ruxolitinib in patients with MF to date, the safety profile was consistent with previous reports, with no new safety concerns identified. This study confirms findings from the COMFORT studies and supports the use of ruxolitinib in patients with platelet counts of 50–100 × 10 9 /l. (ClinicalTrials.gov identifier NCT01493414).
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".