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Record W4391838925 · doi:10.1038/s41409-024-02207-4

Ruxolitinib in patients with graft versus host disease (GvHD): findings from a compassionate use program

2024· article· en· W4391838925 on OpenAlexafffund
Thirupathi Pattipaka, Séverine Sarp, Peyman Nakhaei, Sibel Güneş

Bibliographic record

VenueBone Marrow Transplantation · 2024
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsNovartis (Canada)
FundersNovartis Pharmaceuticals CanadaNovartis Pharma
KeywordsRuxolitinibMedicineInternal medicineGraft-versus-host diseaseGastroenterologyCorticosteroidDiseaseMyelofibrosisBone marrow

Abstract

fetched live from OpenAlex

The ruxolitinib compassionate use (CU) program offered ruxolitinib to patients ≥2 years of age with confirmed steroid-resistant acute or chronic graft-versus-host disease (aGvHD and cGvHD, respectively). Data from 1180 patients (n = 775, 370 and 35 with cGvHD, aGvHD, and non-specified GvHD, respectively) were analyzed. Most patients had severe cGvHD (56%) or stage III/IV aGvHD (70%) disease and had previously received corticosteroids ( > 80%); ruxolitinib was requested primarily as a second-/third-line option. Patients <12 and ≥12 years old most often received the recommended ruxolitinib doses (5 mg twice daily [BID] and 10 mg BID, respectively); however, 23% and 30% of ≥12 year olds with cGvHD and aGvHD, respectively, received the lower dose of 5 mg BID. Notably, corticosteroid usage decreased with ruxolitinib treatment; at the initial ruxolitinib request, 81% and 91% of patients with cGvHD and aGvHD, respectively, were receiving corticosteroids whereas at resupply, 62% and 64%, respectively, were receiving corticosteroids. Eighty two percent of evaluable patients with cGvHD had a complete or partial response to treatment and 56% of evaluable patients with aGvHD had a best response of grade 0/I. These findings demonstrate the rapid and positive effects of ruxolitinib in patients with GvHD in a real-world setting.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2024
Admission routes2
Has abstractyes

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