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Record W4406848242 · doi:10.1038/s41375-024-02482-6

International myeloma working group immunotherapy committee recommendation on sequencing immunotherapy for treatment of multiple myeloma

2025· review· en· W4406848242 on OpenAlexaff
Luciano J. Costa, Rahul Banerjee, Hira Mian, Katja Weisel, Susan Bal, Benjamin A. Derman, Chandramouli Nagarajan, Cesar Rodriguez, Joshua Richter, Matthew J. Frigault, Jing C. Ye, Niels W.C.J. van de Donk, Peter M. Voorhees, Benjamin Puliafito, Nizar J. Bahlis, Rakesh Popat, Wee Joo Chng, P. Joy Ho, Gurbakhash Kaur, Prashant Kapoor, Juan Du, Fredrik Schjesvold, Jesús G. Berdeja, Hermann Einsele, Adam D. Cohen, Joseph Mıkhael, Yelak Biru, S. Vincent Rajkumar, Yi Lin, Thomas G. Martin, Ajai Chari

Bibliographic record

VenueLeukemia · 2025
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of CalgaryInstitute of Cancer ResearchMcMaster University
Fundersnot available
KeywordsChimeric antigen receptorImmunotherapyMultiple myelomaMedicineAntigenOncologyImmunologyInternal medicineImmune system

Abstract

fetched live from OpenAlex

T-cell redirecting therapy (TCRT), specifically chimeric antigen receptor T-cell therapy (CAR T-cells) and bispecific T-cell engagers (TCEs) represent a remarkable advance in the treatment of multiple myeloma (MM). There are several products available around the world and several more in development targeting primarily B-cell maturation antigen (BCMA) and G protein-coupled receptor class C group 5 member D (GRPC5D). The relatively rapid availability of multiple immunotherapies brings the necessity to understand how a certain agent may affect the safety and efficacy of a subsequent immunotherapy so MM physicians and patients can aim at optimal sequential use of these therapies. The International Myeloma Working Group conveyed panel of experts to review patient and disease-related factors affecting efficacy and safety of immunotherapy, summarize existing information on sequencing therapy and provide a series of core recommendations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.083
GPT teacher head0.378
Teacher spread0.294 · 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.

Study designOther design
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

Citations52
Published2025
Admission routes1
Has abstractyes

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