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Record W4411085883 · doi:10.1002/jha2.70072

Final Results of a Phase 2 Multi‐Arm Study of Magrolimab Combinations in Patients With Relapsed/Refractory Multiple Myeloma

2025· article· en· W4411085883 on OpenAlexaff
Barry Paul, Jiří Minařík, Francesca Cottini, Cristina Gasparetto, Jack Khouri, Mitul Gandhi, Jens Hillengaß, Moshe Levy, Michaela Liedtke, Sudhir Manda, Irwindeep Sandhu, Douglas W. Sborov, Ivan Špıčka, Saad Z. Usmani, Mei Dong, Lin Gu, Carmen Oi Ning Leung, Parul Doshi, Christine Chen, Luděk Pour

Bibliographic record

VenueeJHaem · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer CentreAlberta Cancer Foundation
FundersNational Cancer InstituteGilead Sciences
KeywordsDaratumumabMedicinePomalidomideInternal medicineDexamethasoneClinical endpointMultiple myelomaRefractory (planetary science)NeutropeniaAdverse effectOncologyPhases of clinical researchClinical trialLenalidomideToxicity

Abstract

fetched live from OpenAlex

Introduction: Patients with pretreated relapsed/refractory multiple myeloma (RRMM) have a poor prognosis and limited treatment options, underscoring the need for safe treatments with durable efficacy. Methods: This Phase 2 study evaluated magrolimab (Magro) plus daratumumab (Dara) or pomalidomide/dexamethasone (Pd) or carfilzomib/dexamethasone (Kd) in RRMM. The primary efficacy endpoint was objective response rate (ORR). Results: = 11). There were two dose-limiting toxicities: febrile neutropenia (Magro+Dara) and infusion-related reaction (Magro+Pd). Grade ≥ 3 Magro-related adverse event (AE) rates were 64.3% (Magro+Dara), 60.0% (Magro+Pd) and 63.6% (Magro+Kd). Two deaths were AE-related; neither was Magro related. Conclusion: As the study closed early, insights into the clinical profile of Magro combinations in RRMM are limited. Trial Registration: This trial was registered at www.clinicaltrials.gov as #NCT04892446.

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)0.001
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 score0.171
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.335
Teacher spread0.302 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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