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Long-term survival adjusted for treatment crossover in patients (pts) with myelofibrosis (MF) treated with momelotinib (MMB) vs danazol (DAN) in the MOMENTUM trial.

2024· article· en· W4400108991 on OpenAlexaff
Vikas Gupta, Aaron T. Gerds, Alessandro M. Vannucchi, Jean‐Jacques Kiladjian, Claire Harrison, Alisa Urbano, Jireh Huang, Catherine Ellis, Ruben A. Mesa

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineAnemiaRuxolitinibInternal medicineGastroenterologyCrossover studyProportional hazards modelMyelofibrosisSurgeryPlaceboBone marrowPathology

Abstract

fetched live from OpenAlex

6571 Background: Anemia and transfusion dependence affect nearly all pts with MF and are associated with poor prognosis.The phase 3 MOMENTUM trial (NCT04173494) evaluatedMMB—a JAK1, JAK2, and ACVR1 inhibitor—vs DAN (2:1 randomization) in JAK inhibitor (JAKi)–experienced pts with MF and anemia who had symptoms and splenomegaly. While MMB showed spleen, symptom, and anemia benefits vs DAN at wk 24, comparative estimates of long-term overall and leukemia-free survival (OS and LFS) are confounded and may underestimate the MMB effect, as all pts in the DAN arm who entered the open-label phase of the trial crossed over to receive MMB at wk 24. We used a rank-preserving structural failure time (RPSFT) model to estimate the OS and LFS that might have been observed without crossover. Methods: This exploratory analysis evaluated survival over the entire MOMENTUM trial period; most pts entered an extended access study (NCT03441113) after wk 48. The RPSFT model assumes that treatment slows the speed of disease progression and death proportionally regardless of time of crossover. Analyses were conducted with and without recensoring, and CIs were constructed to appropriately account for additional model fitting uncertainty. Results: As of December 29, 2022, 38 (29%) and 20 (31%) deaths had occurred in the MMB and DAN arms, respectively. Risk of death was reduced with MMB vs DAN by 11% (HR, 0.89) with no crossover adjustment, and by 22% (HR, 0.78) and 13% (HR, 0.87) using the RPSFT model with and without recensoring, respectively. Similarly, 40 (31%) and 22 (34%) LFS events had occurred at data cutoff in the MMB and DAN arms, respectively. Risk of an LFS event was reduced with MMB vs DAN by 20% (HR, 0.80) with no crossover adjustment, and by 36% (HR, 0.64) and 23% (HR, 0.77) using the RPSFT model with and without recensoring, respectively (Table). Conclusions: Consistent with the original unadjusted survival analysis, RPSFT models adjusting for the effects of treatment crossover showed prolonged OS and LFS in pts initially randomized to MMB vs those initially randomized to DAN; HRs in favor of MMB were lower after crossover adjustment. While these RPSFT analyses maintain the significance level of the original unadjusted analysis ( P>.05), these results support the trend of long-term survival benefits with MMB vs DAN in JAKi–experienced pts with MF and anemia. Clinical trial information: NCT04173494 . Clinical trial information: NCT03441113 .[Table: see text]

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)0.017
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
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 head0.110
GPT teacher head0.428
Teacher spread0.319 · 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 designNon-randomized trial
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

Citations0
Published2024
Admission routes1
Has abstractyes

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