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Record W1989590412 · doi:10.1038/nature12873

Genetics of rheumatoid arthritis contributes to biology and drug discovery

2013· review· en· W1989590412 on OpenAlexafffund
Yukinori Okada, Di Wu, Gosia Trynka, Towfique Raj, Chikashi Terao, Katsunori Ikari, Yuta Kochi, Koichiro Ohmura, Akari Suzuki, Shinji Yoshida, Robert Graham, Arun Manoharan, Ward Ortmann, Tushar Bhangale, Joshua C. Denny, Robert J. Carroll, Anne E. Eyler, Jeffrey D. Greenberg, Joel M. Kremer, Dimitrios A. Pappas, Lei Jiang, Jian Yin, Lingying Ye, Ding‐Feng Su, Jian Yang, Gang Xie, Ed Keystone, Harm-Jan Westra, Tõnu Esko, Andres Metspalu, Xuezhong Zhou, Namrata Gupta, Daniel B. Mirel, Eli A. Stahl, Dorothée Diogo, Jing Cui, Katherine P. Liao, Michael H. Guo, Keiko Myouzen, Takahisa Kawaguchi, Marieke J. H. Coenen, Piet L. C. M. van Riel, Mart A F J van de Laar, Henk‐Jan Guchelaar, T. Huizinga, Philippe Dieudé, Xavier Mariette, S. Louis Bridges, Alexandra Zhernakova, René E. M. Toes, Paul P. Tak, Corinne Miceli‐Richard, So‐Young Bang, Hye‐Soon Lee, Javier Martı́n, Miguel Á. González‐Gay, Luis Rodríguez‐Rodríguez, Solbritt Rantapää‐Dahlqvist, Lisbeth Ärlestig, Hyon K. Choi, Pilar Galán, Mark Lathrop, John Bowes, Anne Barton, Niek de Vries, Larry W. Moreland, Lindsey A. Criswell, Elizabeth W. Karlson, Atsuo Taniguchi, Ryo Yamada, Michiaki Kubo, Jun S. Liu, Sang‐Cheol Bae, Jane Worthington, Leonid Padyukov, Lars Klareskog, Peter K. Gregersen, Soumya Raychaudhuri, Barbara E. Stranger, Philip L. De Jager, Lude Franke, Peter M. Visscher, Matthew A. Brown, Hisashi Yamanaka, Tsuneyo Mimori, Huji Xu, Timothy W. Behrens, Katherine Siminovitch, Shigeki Momohara, Fumihiko Matsuda, Kazuhiko Yamamoto, Robert M. Plenge

Bibliographic record

VenueNature · 2013
Typereview
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsMcGill University and Génome Québec Innovation CentreUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteNational Key Research and Development Program of ChinaInstituto de Salud Carlos IIICanadian Institutes of Health ResearchVersus ArthritisAssistance publique-Hôpitaux de ParisUniversité Paris 13Medical Research CouncilMedical Research Council CanadaScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of ChinaHanyang UniversityU.S. Public Health ServiceUniversiteit van AmsterdamNational Institutes of HealthRijksuniversiteit GroningenMinistry of Education, Culture, Sports, Science and TechnologySchool of Medicine, Boston UniversityUmeå UniversitetNational Institute for Health and Care ResearchU.S. National Library of MedicineUniversity of PittsburghInstitut National de la Santé et de la Recherche MédicaleGénome QuébecNational Health and Medical Research CouncilUniversité Paris-SudBrigham and Women's HospitalMcGill UniversityJapan Society for the Promotion of ScienceGlaxoSmithKline
KeywordsDrug discoveryRheumatoid arthritisComputational biologyDrugBiologyMedicineEvolutionary biologyGeneticsBioinformaticsPharmacologyImmunology

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.022
GPT teacher head0.370
Teacher spread0.349 · 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 designNot applicable
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

Citations2,473
Published2013
Admission routes2
Has abstractno

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