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Record W4382983620 · doi:10.3390/curroncol30070467

Toxicity Profile of Chimeric Antigen Receptor T-Cell and Bispecific Antibody Therapies in Multiple Myeloma: Pathogenesis, Prevention and Management

2023· review· en· W4382983620 on OpenAlexvenueno aff
Mariam Markouli, Fauzia Ullah, Serhan Ünlü, Najiullah Omar, Nerea Lopetegui‐Lia, Marissa Duco, Faiz Anwer, Shahzad Raza, Danai Dima

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChimeric antigen receptorBispecific antibodyMultiple myelomaAdverse effectMalignancyImmunologyImmunotherapyClinical trialAntigenCytokine release syndromeAntibodyIntensive care medicineImmune systemMonoclonal antibodyInternal medicine

Abstract

fetched live from OpenAlex

Multiple myeloma is the second-most common hematologic malignancy in adults worldwide. Despite ongoing advancement in therapeutic modalities, it remains an incurable disease with a 5-year survival rate of approximately 50%. The recent development and introduction of anti-BCMA immunotherapies into clinical practice, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies, has radically shifted the treatment paradigm. However, despite the promising potential of these therapies for broader application, frequent and significant adverse effects have been reported, both in short- and in long-term settings, requiring increasing awareness and vigilance in the treating team, close monitoring, and prompt interventions with a multidisciplinary approach. In this review, we will discuss the toxicities associated with CAR-T cell and bispecific antibody therapies, focusing on results from major clinical studies and real-world observations. In addition, we will emphasize on effective strategies for prevention, monitoring and management, and provide expert 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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.186
GPT teacher head0.457
Teacher spread0.272 · 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 designNot applicable
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

Citations51
Published2023
Admission routes1
Has abstractyes

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