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Record W2868396400 · doi:10.1038/s41380-018-0079-4

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

2018· review· en· W2868396400 on OpenAlexaff
Jordi Merino, Hassan S. Dashti, Sherly X Li, Chloé Sarnowski, Anne E. Justice, Misa Graff, Constantina Papoutsakis, Caren E. Smith, George Dedoussis, Rozenn N. Lemaître, Mary K. Wojczynski, Satu Männistö, Julius S. Ngwa, Minjung Kho, Tarunveer S. Ahluwalia, Natalia Pervjakova, Denise K. Houston, Claude Bouchard, Tao Huang, Marju Orho‐Melander, Alexis C. Wood, Dennis O. Mook‐Kanamori, Louis Përusse, Craig E. Pennell, Paul S. de Vries, Trudy Voortman, Olivia Li, Stavroula Kanoni, Lynda M. Rose, Terho Lehtimäki, Wei Zhao, Mary F. Feitosa, Jian’an Luan, Nicola M. McKeown, Jennifer A. Smith, Torben Hansen, Niina Eklund, Mike A. Nalls, Tuomo Rankinen, Jinyan Huang, Dena Hernandez, Christina‐Alexandra Schulz, Ani Manichaikul, Ruifang Li‐Gao, Marie‐Claude Vohl, Carol A. Wang, Frank J.A. van Rooij, Jean Shin, Ioanna Panagiota Kalafati, Felix R. Day, Paul M. Ridker, Mika Kähönen, David S. Siscovick, Claudia Langenberg, Wei Zhao, Arne Astrup, Paul Knekt, Melissa García, D. C. Rao, Qibin Qi, Luigi Ferrucci, Ulrika Ericson, John Blangero, Albert Hofman, Zdenka Pausová, Vera Mikkilä, Sharon L. R. Kardia, Oluf Pedersen, Antti Jula, Joanne E. Curran, M. Carola Zillikens, Jorma Viikari, Nita G. Forouhi, José M. Ordovás, John C. Lieske, Harri Rissanen, André G. Uitterlinden, Olli T. Raitakari, Jessica C. Kiefte–de Jong, Josée Dupuis, Jerome I. Rotter, Kari E. North, Robert A. Scott, Michael A. Province, Markus Perola, L. Adrienne Cupples, Stephen T. Turner, Thorkild I. A. Sørensen, Veikko Salomaa, Ching‐Ti Liu, Lu Qi, Stefania Bandinelli, Stephen S. Rich, Renée de Mutsert, Angelo Tremblay, Wendy H. Oddy, Oscar H. Franco, Tomáš Paus, José C. Florez, Panos Deloukas, Leo‐Pekka Lyytikäinen, Daniel I. Chasman, Audrey Y. Chu, Toshiko Tanaka

Bibliographic record

VenueMolecular Psychiatry · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick ChildrenUniversité Laval
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Cancer InstituteNational Institutes of HealthLundbeckfondenNational Eye InstituteBritish Heart FoundationMedical Research CouncilNational Institute for Health and Care Research
KeywordsLocus (genetics)Genome-wide association studyGeneticsBiologyObesityBiobankGenetic variationMeta-analysisBioinformaticsMedicineGenotypeGeneInternal medicineEndocrinologySingle-nucleotide polymorphism

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.230
GPT teacher head0.434
Teacher spread0.204 · 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 designMeta-analysis
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

Citations65
Published2018
Admission routes1
Has abstractno

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