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OP0102 IDENTIFICATION OF NEW RISK LOCI AND PATHWAYS INVOLVED IN GCA PATHOGENESIS BY A GENOME-WIDE STUDY

2023· article· en· W4379517387 on OpenAlexafffund
Gonzalo Borrego‐Yaniz, Lourdes Ortiz‐Fernández, Martin Kerick, Adela Madrid-Paredes, Augusto Vaglio, José Hernández‐Rodríguez, Sarah Mackie, Santos Castañeda, Roser Solans‐Laqué, J. Mestre, Bhaskar Dasgupta, Richard A. Watts, Nader Khalidi, Carol A. Langford, Steven R. Ytterberg, Lorenzo Beretta, Marcello Govoni, Giacomo Emmi, Marco A. Cimmino, T Witte, Thomas Neumann, Julia U. Holle, Verena Schönau, G. Pugnet, T. Papo, Julien Haroche, Alfred Mahr, Luc Mouthon, Øyvind Molberg, Andreas P. Diamantopoulos, Alexandre E. Voskuyl, Thomas Daikeler, Christoph T. Berger, Eleanor J. Molloy, Daniël Blockmans, N. Ortego, Elisabeth Brouwer, Peter Lamprecht, Sebastian Klapa, Carlo Salvarani, Ann W Morgan, J. Martin Ibanez, Ana Márquez

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicOtitis Media and Relapsing Polychondritis
Canadian institutionsMcMaster University
FundersDipartimento di Medicina Sperimentale e Clinica, Università degli Studi di FirenzeUniversitätsklinikum ErlangenUniversitat Autònoma de BarcelonaUniversité Paris DescartesUniversità degli Studi di FerraraAssistance publique-Hôpitaux de ParisUniversité Paris DiderotMedizinischen Hochschule HannoverUniversité de ToulouseMcMaster UniversityFondazione IRCCS Ca' Granda Ospedale Maggiore PoliclinicoInstitut National de la Santé et de la Recherche MédicaleUniversitat de BarcelonaCleveland Clinic
KeywordsGiant cell arteritisGenome-wide association studyImputation (statistics)MedicineDrugBankDiseaseGenetic associationGeneticsBioinformaticsVasculitisBiologyGeneInternal medicineGenotypeSingle-nucleotide polymorphismMissing data

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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

Citations0
Published2023
Admission routes2
Has abstractno

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