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Stroke genetics informs drug discovery and risk prediction across ancestries
Stéphanie Debette, Aniket Mishra, Rainer Malik, Tsuyoshi Hachiya, Tuuli Jürgenson, Shinichi Namba, Masaru Koido, Quentin Le Grand, Frederick Kamanu, Mingyang Shi, Yunye He, Marios K. Georgakis, Ilana Caro, Kristi Krebs, Felix Vaura, Naomi Habib, Bendik S. Winsvold, Yon Ho Jee, Jesper Qvist Thomassen, Vida Abedi, Jara Cárcel‐Márquez, Kuang Lin, Marianne Nygaard, Ganesh Chauhan, Hampton L. Leonard, Chaojie Yang, Ekaterina Yonova-Doing, Maria J. Knol, Tetsuro Ago, Philippe Amouyel, Christopher D. Anderson, Nicole D. Armstrong, Mark K. Bakker, Traci M. Bartz, Joshua C. Bis, Constance Bordes, Sigrid Børte, Anael Cain, Paul M. Ridker, Zhengming Chen, Michael Chong, John W. Cole, Rafael de Cid, Matthias Endres, Leslie Ecker Ferreira, Natalie C. Gasca, Vilmundur Guðnason, Jun Hata, Aki S. Havulinna, Jemma C. Hopewell, Hyacinth Hyacinth, Michael Inouye, Mina A. Jacob, Christina Jeon, Christina Jern, Masahiro Kamouchi, Keith L. Keene, Takanari Kitazono, Steven J. Kittner, Takahiro Konuma, Amit Kumar, Paul Lacaze, Lenore J. Launer, Kaido Lepik, Jiang Li, Liming Li, Ani Manichaikul, Hugh S. Markus, Nicholas Marston, Thomas Meitinger, Braxton D. Mitchell, Felipe A. Montellano, Takayuki Morisaki, Thomas H. Mosley, Mike A. Nalls, Børge G. Nordestgaard, Martin O’Donnell, Yukinori Okada, Guillaume Paré, Annette Peters, Bruce M. Psaty, Stephen S. Rich, Jonathan Rosand, Marc S. Sabatine, Ralph L. Sacco, Danish Saleheen, Else Charlotte Sandset, Muralidharan Sargurupremraj, Makoto Sasaki, Claudia L. Satizábal, Carsten Oliver Schmidt, Atsushi Shimizu, Nicholas L. Smith, Daniel Strbian, Yoichi Sutoh, Kozo Tanno, Steffen Tiedt, Nuria P. Torres‐Aguila, David‐Alexandre Trégouët, Stella Trompet, Anil Tuladhar, Anne Tybjærg‐Hansen, Marion van Vugt, Riina Vibo, Kerri L. Wiggins, Daniel Woo, Huichun Xu, Qiong Yang, Mark Lathrop, Iona Y. Millwood, Christian Gieger, Toshiharu Ninomiya, Hans J. Grabe, J. Wouter Jukema, Ina Rissanen, Sudha Seshadri, W.T. Longstreth, Daniel I. Chasman, Joanna M. M. Howson, Marguerite R. Irvin, Hieab H.H. Adams, Sylvia Wasssertheil‐Smoller, Kaare Christensen, M. Arfan Ikram, Tatjana Rundek, Jerome I. Rotter, Moeen Riaz, Eleanor M. Simonsick, Janika Kõrv, Paulo Henrique Condeixa de França, Myriam Fornage, Ramin Zand, Kameshwar Prasad, Ruth Frikke‐Schmidt, Frank‐Erik de Leeuw, Thomas Liman, Karl Georg Hæusler, Ynte M. Ruigrok, Peter U. Heuschmann, Keum Ji Jung, John‐Anker Zwart, Teemu Niiranen, Christian T. Ruff, Israel Fernández‐Cadenas, Robin Walters, Lili Milani, Martin Dichgans
Bibliographic record
VenueResearch Square · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsHamilton Health SciencesMcGill UniversityThrombosis and Atherosclerosis Research InstituteMcMaster UniversityPopulation Health Research Institute
FundersNational Institute of Neurological Disorders and StrokeFaculty of Medicine and Health, University of SydneyErasmus Universitair Medisch Centrum RotterdamLeids Universitair Medisch CentrumMedical Research CouncilOhio State UniversityUniversity of TokyoAgence Nationale de la RechercheUniversiteit MaastrichtUniversiteit van AmsterdamUniversity of California, San DiegoUniversiteit LeidenUniversity of BristolStatens Serum InstitutCenter for Clinical and Translational Science, Ohio State UniversityInstitut National de la Santé et de la Recherche MédicaleInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementAmsterdam University Medical CentersMassachusetts General HospitalUniversity of MinnesotaNorges Teknisk-Naturvitenskapelige UniversitetMaastricht Universitair Medisch CentrumUniversity of WashingtonUniversitair Medisch Centrum GroningenMcKnight Foundation
KeywordsDrug discoveryDrugComputational biologyGeneticsComputer scienceBiologyBioinformaticsPharmacology
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 machine prediction
Teacher imitationNot 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)0.005
metaresearch head score (Gemma)0.031
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.045
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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 head0.034
GPT teacher head0.380
Teacher spread0.346 · 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 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
Citations10
Published2022
Admission routes1
Has abstractno