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Record W2756071038 · doi:10.1182/blood-2017-06-793141

Kinetics and biomarkers of severe cytokine release syndrome after CD19 chimeric antigen receptor–modified T-cell therapy

2017· article· en· W2756071038 on OpenAlexaff
Kevin A. Hay, Laïla‐Aïcha Hanafi, Daniel Li, Juliane Gust, W. Conrad Liles, Mark M. Wurfel, José A. López, Junmei Chen, Dominic W. Chung, Susanna Harju-Baker, Sindhu Cherian, Xueyan Chen, Stanley R. Riddell, David G. Maloney, Cameron J. Turtle

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteNational Heart, Lung, and Blood InstituteJuno TherapeuticsNational Institutes of Health
KeywordsChimeric antigen receptorCytokine release syndromeCD19ImmunologyReceptorAntigenMedicineCell therapyCytokineImmunotherapyCancer researchBiologyCellImmune systemInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

central memory T cells as independent predictors of CRS. Severe CRS was characterized by hemodynamic instability, capillary leak, and consumptive coagulopathy. Angiopoietin-2 and von Willebrand factor, which are biomarkers of endothelial activation, were increased during severe CRS and also before lymphodepletion in patients who subsequently developed CRS. We describe a classification-tree algorithm to guide studies of early intervention after CAR T-cell infusion for patients at high risk of severe CRS. These data provide a framework for early intervention studies to facilitate safer application of effective CD19 CAR T-cell therapy.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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 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

Citations1,112
Published2017
Admission routes1
Has abstractyes

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