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Gene-Age Interactions in Blood Pressure Regulation: A Large-Scale Investigation with the CHARGE, Global BPgen, and ICBP Consortia
Jeannette Simino, Gang Shi, Joshua C. Bis, Daniel I. Chasman, Georg Ehret, Xiangjun Gu, Xiuqing Guo, Shih-Jen Hwang, Eric J.G. Sijbrands, Albert V. Smith, Germaine C. Verwoert, Jennifer L. Bragg‐Gresham, Gemma Cadby, Peng Chen, Ching‐Yu Cheng, Tanguy Corre, Rudolf A. de Boer, Anuj Goel, Toby Johnson, Chiea Chuen Khor, Behrooz Z. Alizadeh, H. Marike Boezen, Marcel Bruinenberg, Lude Franke, Pim van der Harst, Hans L. Hillege, Melanie M. van der Klauw, Johan Ormel, Dirkje S. Postma, Judith G.M. Rosmalen, Joris P. J. Slaets, Harold Snieder, Ronald P. Stolk, Bruce H. R. Wolffenbuttel, Cisca Wijmenga, Carla Lluís-Ganella, Jian’an Luan, Leo‐Pekka Lyytikäinen, Ilja M. Nolte, Xueling Sim, Siim Sõber, Peter J. van der Most, Niek Verweij, Jing Hua Zhao, Najaf Amin, Eric Boerwinkle, Claude Bouchard, Abbas Dehghan, Guðný Eiríksdóttir, Roberto Elosúa, Oscar H. Franco, Christian Gieger, Tamara B. Harris, Serge Herçberg, Albert Hofman, Andrew D. Johnson, Mika Kähönen, Kay‐Tee Khaw, Zoltán Kutalik, Martin G. Larson, Lenore J. Launer, Li Guo, Jianjun Liu, Kiang Liu, Alanna C. Morrison, Rick Twee‐Hee Ong, George Papanicolau, Brenda W.J.H. Penninx, Bruce M. Psaty, Leslie J. Raffel, Olli T. Raitakari, Kenneth Rice, Fernando Rivadeneira, Lynda M. Rose, Serena Sanna, Robert A. Scott, David S. Siscovick, André G. Uitterlinden, Dhananjay Vaidya, Ramachandran S. Vasan, Eranga N. Vithana, Uwe Völker, Henry Völzke, Hugh Watkins, Terri L. Young, Tin Aung, Murielle Bochud, Martin Farrall, Catharina A. Hartman, Maris Laan, Edward G. Lakatta, Terho Lehtimäki, Ruth J. F. Loos, Gavin Lucas, Pierre Meneton, Lyle J. Palmer, Rainer Rettig, E Shyong Tai, Yik-Ying Teo, Nicholas J. Wareham, Tien Yin Wong, Myriam Fornage, Vilmundur Guðnason, Daniel Levy, Walter Palmas, Paul M. Ridker, Jerome I. Rotter, Cornelia M. van Duijn, Jacqueline C.M. Witteman, Aravinda Chakravarti, D. C. Rao
Bibliographic record
VenueThe American Journal of Human Genetics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsLunenfeld-Tanenbaum Research InstituteOntario Institute for Cancer Research
FundersNational Center for Advancing Translational SciencesMedical Research CouncilIC Design Education CenterNational Institute for Health and Care ResearchCancer Research UKYale UniversityBiogenNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteMedtronicAmgen
KeywordsSNPGenome-wide association studySingle-nucleotide polymorphismLocus (genetics)BiologyBlood pressureGeneticsGeneEvolutionary biologyGenotypeEndocrinology
Abstract
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 imitationNot 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)0.001
metaresearch head score (Gemma)0.000
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 score0.119
Threshold uncertainty score0.282
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.011
GPT teacher head0.265
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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
Citations149
Published2014
Admission routes1
Has abstractno