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Record W4387115637 · doi:10.1111/ejh.14100

Long‐term survivorship care after <scp>CAR‐T</scp> cell therapy

2023· review· en· W4387115637 on OpenAlexaff
Robert Puckrin, Kareem Jamani, Víctor H. Jiménez‐Zepeda

Bibliographic record

VenueEuropean Journal Of Haematology · 2023
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsInstitute of Cancer ResearchUniversity of Calgary
Fundersnot available
KeywordsChimeric antigen receptorCytokine release syndromeMedicineTocilizumabSurvivorship curvePopulationCAR T-cell therapyImmune systemDiseaseImmunologyIntensive care medicineImmunotherapyCell therapyT cellBioinformaticsCellInternal medicineBiology

Abstract

fetched live from OpenAlex

While cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome are well-recognized acute toxicities of chimeric antigen receptor (CAR) T cell therapy, these complications have become increasingly manageable by protocolized treatment algorithms incorporating the early administration of tocilizumab and corticosteroids. As CAR-T cell therapy expands to new disease indications and the number of long-term survivors steadily increases, there is growing recognition of the need to appropriately evaluate and manage the late effects of CAR-T cell therapy, including late-onset or persistent neurotoxicity, prolonged cytopenias, delayed immune reconstitution and infections, subsequent malignancies, organ dysfunction, psychological distress, and fertility implications. In this review, we provide a practical approach to the long-term survivorship care of the CAR-T cell recipient, with a focus on the optimal strategies to address the common and challenging late complications affecting this unique population.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.002

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 head0.100
GPT teacher head0.368
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

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

Citations20
Published2023
Admission routes1
Has abstractyes

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