MétaCan
Menu
Back to cohort
Record W3049048202 · doi:10.1016/j.cell.2020.08.025

Loss of Bcl-6-Expressing T Follicular Helper Cells and Germinal Centers in COVID-19

2020· article· en· W3049048202 on OpenAlexaff
Naoki Kaneko, Hsiao-Hsuan Kuo, Julie Boucau, Jocelyn R. Farmer, Hugues Allard‐Chamard, Vinay S. Mahajan, Alicja Piechocka‐Trocha, Kristina Lefteri, Matthew R. Osborn, Julia Bals, Yannic C. Bartsch, Nathalie Bonheur, Timothy M. Caradonna, Joshua M Chevalier, Fatema Z. Chowdhury, Thomas Diefenbach, Kevin Einkauf, Jon Fallon, Jared Feldman, Kelsey Finn, Pilar García‐Broncano, Ciputra Adijaya Hartana, Blake M. Hauser, Chenyang Jiang, Paulina Kapłonek, Marshall Karpell, Eric C. Koscher, Xiaodong Lian, Hang Liu, Jin-Qing Liu, Ngoc L. Ly, Ashlin R. Michell, Yelizaveta Rassadkina, Kyra Seiger, Libera Sessa, Sally Shin, Nishant K. Singh, Weiwei Sun, Xiaoming Sun, Hannah Ticheli, Michael T. Waring, Alex Zhu, Galit Alter, Jonathan Z. Li, Daniel Lingwood, Aaron G. Schmidt, Mathias Lichterfeld, Bruce D. Walker, Xu G. Yu, Robert F. Padera, Shiv Pillai

Bibliographic record

VenueCell · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversité de Sherbrooke
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Allergy and Infectious DiseasesNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNational Institute on Drug AbuseNational Institutes of Health
KeywordsBiologyGerminal centerCoronavirus disease 2019 (COVID-19)VirologyFollicular phase2019-20 coronavirus outbreakCell biologyMolecular biologyImmunologyCancer researchGeneticsAntibodyB cellInternal medicineDiseaseOutbreakInfectious disease (medical specialty)

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)0.000
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.042
GPT teacher head0.332
Teacher spread0.290 · 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

Citations782
Published2020
Admission routes1
Has abstractno

Explore more

Same venueCellSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207