The impact of ruxolitinib on thrombosis in patients with polycythemia vera and myelofibrosis
Bibliographic record
Abstract
The Food and Drug Administration approval of ruxolitinib for treatment of myelofibrosis and polycythemia vera has changed the management of patients with myeloproliferative neoplasms. Yet the impact of this therapy on risk of thrombosis, a major cause of morbidity and mortality among these patients, remains unknown. The aim of this study was to evaluate the impact of ruxolitinib on the risk of thrombosis among patients with polycythemia vera or myelofibrosis. Following identification of randomized controlled trials comparing ruxolitinib to standard care or placebo, rates of thrombosis, including venous and arterial thrombosis, were analyzed using fixed effects models. Rates of thrombosis were significantly lower among patients treated with ruxolitinib [risk ratio 0.45, 95% confidence interval (CI) 0.23-0.88]. Subgroup analysis of venous and arterial thrombosis demonstrated similar risk ratios, which did not reach statistical significance (risk ratio 0.46, 95% CI 0.14-1.48 and RR 0.42, 95% CI 0.18-1.01, respectively). In conclusion, our analysis suggests that JAK2 inhibition with ruxolitinib decreases the risk of arterial and/or venous thrombosis in patients with polycythemia vera or myelofibrosis. These findings will require confirmation in a prospective study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".