The clinical benefit of ruxolitinib across patient subgroups: analysis of a placebo‐controlled, Phase <scp>III</scp> study in patients with myelofibrosis
Bibliographic record
Abstract
Myelofibrosis (MF) patients can present with a wide spectrum of disease characteristics. We analysed the consistency of ruxolitinib efficacy across patient subgroups in the COntrolled MyeloFibrosis Study With ORal JAK Inhibitor Treatment (COMFORT-I,) a double-blind trial, where patients with intermediate-2 or high-risk MF were randomized to twice-daily oral ruxolitinib (n = 155) or placebo (n = 154). Subgroups analysed included MF subtype (primary, post-polycythaemia vera, post-essential thrombocythaemia), age (≤65, > 65 years), International Prognostic Scoring System risk group, baseline Eastern Cooperative Oncology Group performance status (0, 1, ≥2), JAK2 V617F mutation (positive, negative), baseline haemoglobin level (≥100, <100 g/l), baseline platelet count (100-200 × 10(9)/l, >200 × 10(9)/l), baseline palpable spleen size (≤10, >10 cm), and baseline quartile of spleen volume and Total Symptom Score (TSS; Q1 = lowest, Q4 = highest). Mean percentage change from baseline to week 24 in spleen volume and TSS were calculated for ruxolitinib and placebo in each subgroup. Overall survival was estimated by Kaplan-Meier method according to original randomization group. In ruxolitinib-treated patients, reductions in spleen volume and TSS and evidence of improved survival relative to placebo across subgroups were consistent with those seen in the COMFORT-I population, confirming that ruxolitinib is an effective therapy for the spectrum of MF patients studied in COMFORT-I.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".