MétaCan
Menu
Back to cohort
Record W2609196580 · doi:10.1080/2162402x.2017.1321184

Mapping the human T cell repertoire to recurrent driver mutations in MYD88 and EZH2 in lymphoma

2017· article· en· W2609196580 on OpenAlexafffund
Julie S. Nielsen, Andrew Chang, Darin A. Wick, Colin G. Sedgwick, Zusheng Zong, Andrew J. Mungall, Spencer D. Martin, Natalie N. Kinloch, Susann Ott-Langer, Zabrina L. Brumme, Steven P. Treon, Joseph M. Connors, Randy D. Gascoyne, John R. Webb, Brian Berry, Ryan D. Morin, Nicol Macpherson, Brad H. Nelson

Bibliographic record

VenueOncoImmunology · 2017
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of British ColumbiaCanada's Michael Smith Genome Sciences CentreUniversity of VictoriaSimon Fraser UniversityBC Cancer Agency
FundersCanadian Institutes of Health Research
KeywordsLymphomaCD8Human leukocyte antigenImmunologyCytotoxic T cellAntigenImmune systemT cellBiologyCancer researchMedicineGenetics

Abstract

fetched live from OpenAlex

IPIKYKA-specific T cells in seven other HLA-B*07:02-positive donors, including two lymphoma patients. Thus, healthy donors harbor T cells specific for common driver mutations in lymphoma. However, such responses appear to be rare due to the combined limitations of antigen processing, HLA restriction, and T cell repertoire size, highlighting the need for highly individualized approaches for selecting targets.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.827
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.051
GPT teacher head0.345
Teacher spread0.295 · 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.

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

Citations23
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueOncoImmunologySame topicCAR-T cell therapy researchFrench-language works237,207