A Prognostic Model to Predict Ruxolitinib Discontinuation and Death in Patients with Myelofibrosis
Bibliographic record
Abstract
Most patients with myelofibrosis (MF) discontinue ruxolitinib (JAK1/JAK2 inhibitor) in the first 5 years of therapy due to therapy failure. As the therapeutic possibilities of MF are expanding, it is critical to identify patients predisposed to early ruxolitinib monotherapy failure and worse outcomes. We investigated predictors of early ruxolitinib discontinuation and death on therapy in 889 patients included in the “RUX-MF” retrospective study. Overall, 172 patients were alive on ruxolitinib after ≥5 years (long-term ruxolitinib, LTR), 115 patients were alive but off ruxolitinib after ≥5 yrs (short-term RUX, STR), and 123 patients died while on ruxolitinib after <5 yrs (early death on ruxolitinib, EDR). The cumulative incidence of the blast phase was similar in LTR and STR patients (p = 0.08). Overall survival (OS) was significantly longer in LTR pts (p = 0.002). In multivariate analysis, PLT < 100 × 109/L, Hb < 10 g/dL, primary MF, absence of spleen response at 3 months and ruxolitinib starting dose <10 mg BID were associated with higher probability of STR. Assigning one point to each significant variable, a prognostic model for STR (STR-PM) was built, and three groups were identified: low (score 0–1), intermediate (score 2), and high risk (score ≥ 3). The STR-PM may identify patients at higher risk of failure with ruxolitinib monotherapy who should be considered for alternative frontline strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".