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Record W4308211464 · doi:10.1038/s41588-022-01213-w

Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis

2022· review· en· W4308211464 on OpenAlexaff
Kazuyoshi Ishigaki, Saori Sakaue, Chikashi Terao, Yang Luo, Kyuto Sonehara, Kensuke Yamaguchi, Tiffany Amariuta, Chun Lai Too, Vincent A. Laufer, Ian C. Scott, Sébastien Viatte, Meiko Takahashi, Koichiro Ohmura, Akira Murasawa, Motomu Hashimoto, Hiromu Ito, Samer Hammoudeh, Samar Al Emadi, Basel Masri, Hussein Halabi, Humeira Badsha, Imad Uthman, Xin Wu, Lin Li, Ting Li, Darren Plant, Anne Barton, Gisela Orozco, Suzanne Verstappen, John Bowes, Alex J. MacGregor, Suguru Honda, Masaru Koido, Kohei Tomizuka, Yoichiro Kamatani, Hiroaki Tanaka, Eiichi Tanaka, Akari Suzuki, Yuichi Maeda, Kenichi Yamamoto, Satoru Miyawaki, Gang Xie, Jinyi Zhang, Christopher I. Amos, Edward Keystone, Gertjan Wolbink, Irene van der Horst‐Bruinsma, Jing Cui, Katherine P. Liao, Robert J. Carroll, Hye‐Soon Lee, So‐Young Bang, Katherine Siminovitch, Niek de Vries, Lars Alfredsson, Solbritt Rantapää‐Dahlqvist, Elizabeth W. Karlson, Sang‐Cheol Bae, Robert P. Kimberly, Jeffrey C. Edberg, Xavier Mariette, T. Huizinga, Philippe Dieudé, Matthias Schneider, Martin Kerick, Joshua C. Denny, Koichi Matsuda, Keitaro Matsuo, Tsuneyo Mimori, Fumihiko Matsuda, Keishi Fujio, Yoshiya Tanaka, Atsushi Kumanogoh, Matthew Traylor, Cathryn M. Lewis, Stephen Eyre, Huji Xu, Richa Saxena, Thurayya Arayssi, Yuta Kochi, Katsunori Ikari, Masayoshi Harigai, Peter K. Gregersen, Kazuhiko Yamamoto, S. Louis Bridges, Leonid Padyukov, Javier Martı́n, Lars Klareskog, Yukinori Okada, Soumya Raychaudhuri

Bibliographic record

VenueNature Genetics · 2022
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research Institute
FundersNational Cancer InstituteNational Human Genome Research InstituteMoonshot Research and Development ProgramNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNIHR Maudsley Biomedical Research CentreNational Institutes of HealthKementerian Kesihatan MalaysiaVersus ArthritisKanae Foundation for the Promotion of Medical ScienceInstituto de Salud Carlos IIIAstellas Foundation for Research on Metabolic DisordersAstellas PharmaVetenskapsrådetFoundation for the National Institutes of HealthFonds National de la Recherche LuxembourgWellcome TrustEuropean League Against RheumatismKing's College LondonTokyo Medical and Dental UniversityNational Institute for Health and Care ResearchMinisterio de Ciencia e InnovaciónJapan Agency for Medical Research and DevelopmentQatar National Research FundQatar FoundationNational Center for Advancing Translational SciencesDepartment of Health and Social CareJapan Society for the Promotion of ScienceMochida Memorial Foundation for Medical and Pharmaceutical Research
KeywordsGenome-wide association studyBiologyGenetic genealogyGenetic associationGeneticsGenetic architectureRheumatoid arthritis1000 Genomes ProjectEvolutionary biologyComputational biologySingle-nucleotide polymorphismGeneGenotypeQuantitative trait locusPopulationMedicineImmunology

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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.068
GPT teacher head0.377
Teacher spread0.309 · 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
GenreReview

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

Citations348
Published2022
Admission routes1
Has abstractno

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