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Record W4404749043 · doi:10.1016/j.immuni.2024.10.014

Impaired development of memory B cells and antibody responses in humans and mice deficient in PD-1 signaling

2024· article· en· W4404749043 on OpenAlexaff
Masato Ogishi, Koji Kitaoka, Kim L. Good‐Jacobson, Darawan Rinchai, Baihao Zhang, Jun Wang, Vincent Gies, Geetha Rao, Tina Nguyen, Danielle T. Avery, Taushif Khan, Megan E. Smithmyer, Joseph Mackie, Rui Yang, Andrés A. Arias, Takaki Asano, Khoren Ponsin, Matthieu Chaldebas, Peng Zhang, Jessica N. Peel, Jonathan Bohlen, Romain Lévy, Simon J. Pelham, Wei-Te Lei, Ji Eun Han, Iris Fagniez, Maya Chrabieh, Candice Lainé, David Langlais, Conor Gruber, Fatima Al Ali, Mahbuba Rahman, Caner Aytekin, Basilin Benson, Matthew J. Dufort, Clara Domingo‐Vila, Kunihiko Moriya, Mark J. Shlomchik, Gülbû Uzel, Paul Gray, Daniel Suan, Kahn Preece, Ignatius Chua, Satoshi Okada, Shunsuke Chikuma, Hiroshi Kiyonari, Timothy Tree, Dusan Bogunovic, Philippe Gros, Nico Marr, Cate Speake, Richard A. Oram, Vivien Béziat, Jacinta Bustamante, Laurent Abel, Bertrand Boisson, Anne‐Sophie Korganow, S. Cindy, Matthew B. Johnson, Kenji Chamoto, Stéphanie Boisson‐Dupuis, Tasuku Honjo, Jean‐Laurent Casanova, Stuart G. Tangye

Bibliographic record

VenueImmunity · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsMcGill University Health CentreMcGill Genome Centre
FundersVermont Agency of Natural ResourcesNational Institutes of HealthNew South Wales GovernmentDivision of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious DiseasesGlenn Foundation for Medical ResearchHonjo International Scholarship FoundationUniversidad de AntioquiaNational Health and Medical Research CouncilSCOR Corporate Foundation for ScienceMinistério da Ciência, Tecnologia e InovaçãoInstitut National de la Santé et de la Recherche MédicaleFondation pour la Recherche MédicaleNational Institute on Handicapped ResearchUniversité Paris-SaclayNational Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesAustralian GovernmentAgence Nationale de la RechercheTasmanian Department of HealthRockefeller UniversityResearch EnglandIcahn School of Medicine at Mount SinaiMinisterio de Ciencia, Tecnología, Conocimiento e InnovaciónDiabetes UKMinistry of Health, Labour and WelfareSt. Giles FoundationNational Center for Advancing Translational SciencesMedical Research CouncilLeona M. and Harry B. Helmsley Charitable TrustImmune Deficiency FoundationMinisterio de Ciencia, Tecnología e Innovación
KeywordsBiologyAntibodySignal transductionImmunologyCell biology

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractno

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