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Biomarkers of Dietary Omega-6 Fatty Acids and Incident Cardiovascular Disease and Mortality
Matti Marklund, Jason Wu, Fumiaki Imamura, Liana C. Del Gobbo, Amanda M. Fretts, Janette de Goede, Peilin Shi, Nathan Tintle, Maria Wennberg, Stella Aslibekyan, Tzu-An Chen, Marcia C. de Oliveira Otto, Yoichiro Hirakawa, Helle Højmark Eriksen, Janine Kröger, Federica Laguzzi, Maria Lankinen, Rachel A. Murphy, Kiesha Prem, Cécilia Samieri, Jyrki K. Virtanen, Alexis C. Wood, Kerry LM Wong, Wei-Sin Yang, Xia Zhou, Ana Baylín, Jolanda M.A. Boer, Ingeborg A. Brouwer, Hannia Campos, Paulo H. M. Chaves, Kuo‐Liong Chien, Ulf dé Fairé, Luc Djoussé, Guðný Eiríksdóttir, Naglaa El-Abbadi, Nita G. Forouhi, J. Michael Gaziano, Johanna M. Geleijnse, Bruna Gigante, Graham G. Giles, Eliseo Güallar, Vilmundur Guðnason, Tamara B. Harris, William S. Harris, Catherine Helmer, Mai-Lis Hellénius, Allison Hodge, Frank B. Hu, Paul F. Jacques, Jan-Håkan Jansson, Anya Kalsbeek, Kay‐Tee Khaw, Woon‐Puay Koh, Markku Laakso, Karin Leander, Hung-Ju Lin, Lars Lind, Robert Luben, Juhua Luo, Barbara McKnight, Jaakko Mursu, Toshiharu Ninomiya, Kim Overvad, Bruce M. Psaty, Eric B. Rimm, Matthias B. Schulze, David S. Siscovick, Michael S. Nielsen, Albert V. Smith, Brian T. Steffen, Lyn M. Steffen, Qi Sun, Johan Sundström, Michael Y. Tsai, H Tunstall-Pedoe, Matti Uusitupa, Rob M. van Dam, Jenna Veenstra, W. M. Monique Verschuren, Walter C. Willett, Mark Woodward, Jian-Min Yuan, Renata Micha, Rozenn N. Lemaître, Dariush Mozaffarian, Ulf Risérus
Bibliographic record
VenueCirculation · 2019
Typereview
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of MelbourneNational Human Genome Research InstituteMcKnight FoundationUppsala UniversitetMedical Research CouncilHerbert Wertheim College of Medicine, Florida International UniversityJohns Hopkins Bloomberg School of Public HealthUniversity of New South WalesNational Institute on Alcohol Abuse and AlcoholismAgricultural Research ServiceNational Institutes of HealthInstitut National de la Santé et de la Recherche MédicaleNational Taiwan UniversitySchool of Medicine, Stanford UniversityHjartaverndItä-Suomen YliopistoNational Heart, Lung, and Blood InstituteAalborg UniversitetshospitalUmeå UniversitetNational Institute on AgingKarolinska InstitutetYale UniversityHarvard T.H. Chan School of Public HealthNational Institute for Health and Care ResearchJohns Hopkins UniversityAmarin CorporationUniversity of South DakotaSchool of Public Health, University of MichiganGeorge Institute for Global HealthNational University of SingaporeCancer Research UKWageningen University and ResearchDuke-NUS Medical SchoolBill and Melinda Gates FoundationUniversity of WashingtonU.S. Department of Veterans AffairsUniversity of MinnesotaHáskóli ÍslandsBritish Heart FoundationNational Taiwan University HospitalAalborg UniversitetU.S. Department of AgricultureBrigham and Women's HospitalNational Cancer InstituteSouth Dakota Governor's Office of Economic DevelopmentFlorida International University
KeywordsMedicineDiseaseOmegaInternal medicineCardiology
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
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)0.018
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.095
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 head0.102
GPT teacher head0.368
Teacher spread0.266 · 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 designSystematic review
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
Citations330
Published2019
Admission routes1
Has abstractyes