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Record W4395667837 · doi:10.1038/s41588-024-01725-7

Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions

2024· review· en· W4395667837 on OpenAlexaff
Mark P. Purdue, Diptavo Dutta, Mitchell J. Machiela, Bryan R. Gorman, Timothy Winter, Dayne Okuhara, Sara Cleland, Aida Ferreiro-Iglesias, Paul Scheet, Aoxing Liu, Chao Wu, Samuel O. Antwi, James Larkin, Stênio C Zequi, Maxine Sun, Keiko Hikino, Ali Hajiran, Keith A. Lawson, Flavio Mavignier Cárcano, Odile Blanchet, Brian Shuch, Kenneth G. Nepple, G. Margue, Debasish Sundi, W. Ryan Diver, Maria Aparecida Azevedo Koike Folgueira, Adrie van Bokhoven, Florencia Neffa, Kevin M Brown, Jonathan N. Hofmann, Jongeun Rhee, Meredith Yeager, Nathan R Cole, Belynda D Hicks, Michelle R Manning, Amy Hutchinson, N. Rothman, Wen‐Yi Huang, W. Marston Linehan, Adriana Lori, Matthieu Ferragu, Merzouka Zidane-Marinnes, Sérgio Serrano, Wesley J Magnabosco, Ana Paula Vilas, Ricardo Decia, Florencia Carusso, Laura S Graham, Kyra Anderson, Mehmet Asım Bilen, Cletus A. Arciero, Isabelle Pellegrin, Solène Ricard, Ghislaine Scélo, Rosamonde E. Banks, Naveen S Vasudev, Naeem Soomro, Grant D. Stewart, Adebanji Adeyoju, Stephen Bromage, David Hrouda, Norma Gibbons, Poulam M. Patel, Mark Sullivan, Andrew Protheroe, Francesca I Nugent, Michelle J Fournier, Xiaoyu Zhang, Lisa J Martin, Maria Komisarenko, Timothy Eisen, Sonia Cunningham, Denise C. Connolly, Robert G. Uzzo, Давид Заридзе, Anush Mukeria, Ivana Holcátová, Anna Horňáková, Lenka Foretová, Vladimir Janout, Dana Mates, Viorel Jinga, Ștefan Rașcu, Mirjana Mijušković, Slaviša Savić, Saša Milosavljević, Valérie Gaborieau, Behnoush Abedi‐Ardekani, James McKay, Mattias Johansson, Larry Phouthavongsy, L. Anne Hayman, Jason Li, Ilinca Lungu, Stephania M Bezerra, Aline G. de Souza, Cláudia Tarcila Gomes Sares, Rodolfo B Reis, Fábio Pescarmona Gallucci, Mauricio D Cordeiro, Mark Pomerantz, Gwo‐Shu M. Lee, Matthew L. Freedman, Anhyo Jeong, Samantha Greenberg, Alejandro Sanchez, R. Houston Thompson, Vidit Sharma, David D Thiel, Colleen T. Ball, Diego Abreu, Elaine T. Lam, William Carlos Nahas, Viraj A. Master, Alpa V. Patel, Jean‐Christophe Bernhard, Neal D. Freedman, Pierre Bigot, Rui Manuel Reis, Leandro M. Colli, Antonio Finelli, Brandon J. Manley, Chikashi Terao, Toni K. Choueiri, Dirce M Carraro, Richard S. Houlston, Jeanette E. Eckel‐Passow, Philip H Abbosh, Andrea Ganna, Paul Brennan, Jian Gu, Stephen J. Chanock

Bibliographic record

VenueNature Genetics · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsOntario Institute for Cancer ResearchPrincess Margaret Cancer CentreUniversity Health Network
FundersMedical Research CouncilNational Institutes of HealthWorld Health Organization
KeywordsBiologyKidney cancerGenome-wide association studyOdds ratioLocus (genetics)Expression quantitative trait lociQuantitative trait locusGeneticsSingle-nucleotide polymorphismGenetic associationCancerGenetic architectureKidney diseaseGeneGenotypeInternal medicineMedicineEndocrinology

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)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.044
GPT teacher head0.380
Teacher spread0.336 · 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

Citations48
Published2024
Admission routes1
Has abstractno

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