MétaCan
Menu
← Back to cohort
Record W2734515449 · doi:10.1016/j.ymthe.2017.07.004

Long-Duration Complete Remissions of Diffuse Large B Cell Lymphoma after Anti-CD19 Chimeric Antigen Receptor T Cell Therapy

2017· article· en· W2734515449 on OpenAlexfundno aff
James N. Kochenderfer, Robert Somerville, Tangying Lu, James C. Yang, Richard M. Sherry, Steven A. Feldman, Lori McIntyre, Adrian Bot, John M. Rossi, Norris Lam, Steven A. Rosenberg

Bibliographic record

VenueMolecular Therapy · 2017
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
FundersResearch and DevelopmentGraduate Women – New South WalesNorthwest UniversityDepartment of Health and Social CareNIH Clinical CenterNational Cancer InstituteNational Institutes of HealthU.S. Department of Health and Human ServicesGovernment of CanadaGeoffrey Beene Cancer Research CenterNational Institute for Health and Care ResearchNational Cancer Research Institute
KeywordsChimeric antigen receptorCD19LymphomaMedicineAntigenReceptorImmunologyB cellCellCancer researchBiologyT cellAntibodyInternal medicineImmune system

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.303
Teacher spread0.277 · 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

Citations280
Published2017
Admission routes1
Has abstractno

Explore more

Same venueMolecular Therapy→Same topicCAR-T cell therapy research→French-language works237,207→