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Record W4389229959 · doi:10.1182/blood-2023-185582

Ruxolitinib Versus Best Available Therapy for Polycythemia Vera: A Systematic Review and Meta-Analysis

2023· review· en· W4389229959 on OpenAlexaff
M. Mora, Catharina Ribeiro Guimaraes, Farhan Afzal, Amanda Godoi, Andrés Valenzuela

Bibliographic record

VenueBlood · 2023
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRuxolitinibMedicineInternal medicinePolycythemia veraPopulationMeta-analysisMyelofibrosisMyeloproliferative neoplasmRandomized controlled trialAnemiaConfidence intervalBone marrow

Abstract

fetched live from OpenAlex

Background: Polycythemia vera (PV) is characterized by dysregulated Janus kinase (JAK) activation, leading to excessive production of blood cells, increased risk of thrombotic and hemorrhagic events, and an impairment on the quality of life of patients. Ruxolitinib, a JAK1/JAK2 inhibitor, has shown promising results in recent trials, but its comparative efficacy to best available therapy (BAT) remains unclear. We aimed to perform a systematic review and meta-analysis comparing the efficacy and safety of Ruxolitinib versus BAT for patients with PV. Methods: We systematically searched PubMed, Embase, and Cochrane Central for clinical studies published up to July 1st comparing Ruxolitinib to BAT. BAT was defined as hydroxyurea, interferon or pegylated interferon, pipobroman and anagrelide. Efficacy outcomes assessed were complete response to therapy and reduction in the Myeloproliferative Neoplasm Symptom Assessment Form (MPN-SAF) score; safety outcomes were thromboembolism, anemia, and herpes. We pooled outcomes as risk ratios (RR) with 95% confidence intervals (CI). Statistical analysis was performed with Review Manager version 5.4.1. Heterogeneity was assessed with I² statistics. The protocol was registered in PROSPERO (ID: CRD42023427977). Results: A total of five studies met our inclusion criteria, of which four were randomized trials. Our analysis encompassed a population of 684 patients, including 336(49%) allocated to the Ruxolitinib group, and 348(51%) allocated to the BAT group. Ruxolitinib had a significantly greater response to treatment compared to BAT (RR 1.97; 95% CI 1.03 -3.77; p=0.04; I²=60%; Fig 1A) and significantly reduced MPN-SAF score by at least 50% (RR 2.50; 95% CI 1.29-4.86; p=0.007; I²=76%; Fig 1B). Additionally, use of Ruxolitinib showed a significantly higher incidence of herpes infection (RR 3.57; 95% CI 1.27 -10.04; p=0.02; I²=0%; Fig 1C) and anemia (RR 1.97; 95% CI 1.15-3.37; p=0.01; I²=50%; Fig 1D). There were no significant differences between the two treatment groups regarding thromboembolic events (p=0.07; Fig 1E). Conclusion: Our meta-analysis reveals that Ruxolitinib exhibits superior efficacy in achieving a complete response for PV patients compared to BAT and in reducing MPN-SAF score by ≥50%. While no significant difference was observed in thromboembolic events, Ruxolitinib demonstrated significant risk of anemia and herpes infection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0230.031
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.257
GPT teacher head0.409
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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