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Record W2079362912 · doi:10.1038/ng.787

Genome-wide association study identifies susceptibility loci for IgA nephropathy

2011· article· en· W2079362912 on OpenAlexaff
Ali G. Gharavi, Krzysztof Kiryluk, Murim Choi, Yifu Li, Ping Hou, Jingyuan Xie, Simone Sanna‐Cherchi, Clara J. Men, Bruce A. Julian, Robert Wyatt, Jan Novák, John Cijiang He, Haiyan Wang, Jicheng Lv, Li Zhu, Weiming Wang, Zhaohui Wang, Kasuhito Yasuno, Murat Günel, Shrikant Mane, Sheila Umlauf, Irina Tikhonova, Isabel Beerman, Silvana Savoldi, Riccardo Magistroni, Gian Marco Ghiggeri, Monica Bodria, Francesca Lugani, Pietro Ravani, Claudio Ponticelli, Landino Allegri, Giuliano Boscutti, Giovanni M. Frascà, Alessandro Amore, Licia Peruzzi, Rosanna Coppo, Claudia Izzi, Battista Fabio Viola, E. Prati, Maurizio Salvadori, Renzo Mignani, Loreto Gesualdo, Francesca Bertinetto, Paola Mesiano, Antonio Amoroso, Francesco Scolari, Nan Chen, Hong Zhang, Richard P. Lifton

Bibliographic record

VenueNature Genetics · 2011
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of Calgary
FundersNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesHoward Hughes Medical Institute
KeywordsBiologyGenome-wide association studyNephropathyGeneticsAlleleLocus (genetics)Odds ratioGenetic associationMinor allele frequencyGenetic variationAllele frequencyImmunologySingle-nucleotide polymorphismGenotypeGeneInternal medicineDiabetes mellitusEndocrinologyMedicine

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.281
Teacher spread0.263 · 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

Citations624
Published2011
Admission routes1
Has abstractno

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