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Record W3028238352 · doi:10.1016/j.archger.2020.104112

Education and the moderating roles of age, sex, ethnicity and apolipoprotein epsilon 4 on the risk of cognitive impairment

2020· article· en· W3028238352 on OpenAlexaff
Steve R. Makkar, Darren M. Lipnicki, John D. Crawford, Nicole A. Kochan, E. Costa, Maria Fernanda Lima‐Costa, Breno S. Diniz, Carol Brayne, Blossom C. M. Stephan, Fiona E. Matthews, Juan J. Llibre Rodríguez, Jorge J. Llibre‐Guerra, Adolfo J. Valhuerdi-Cepero, Richard B. Lipton, Mindy J. Katz, Andrea R. Zammit, Karen Ritchie, Sophie Carles, Isabelle Carrière, Nikolaos Scarmeas, Mary Yannakoulia, Mary H. Kosmidis, Linda Lam, Ada W. T. Fung, Wai Chi Chan, Antonio Guaita, Roberta Vaccaro, Annalisa Davin, Ki Woong Kim, Ji Won Han, Seung Wan Suh, Steffi G. Riedel‐Heller, Susanne Roehr, Alexander Pabst, Mary Ganguli, Tiffany F. Hughes, Erin Jacobsen, Kaarin J. Anstey, Nicolas Cherbuin, Mary N. Haan, Allison E. Aiello, Kristina Dang, Shuzo Kumagai, Kenji Narazaki, Sanmei Chen, Tze Pin Ng, Qi Gao, Ma Shwe Zin Nyunt, Kenichi Meguro, Satoshi Yamaguchi, Hiroshi Ishii, António Lobo, Elena Lobo, Concepción de la Cámara, Henry Brodaty, Julian N. Trollor, Yvonne Leung, Jessica Lo, Perminder S. Sachdev

Bibliographic record

VenueArchives of Gerontology and Geriatrics · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCanada Research ChairsUniversity of TorontoCentre for Addiction and Mental Health
FundersFondo Nacional de Desarrollo Científico y TecnológicoNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeNational Institute on AgingNational Medical Research CouncilNational Health and Medical Research CouncilAllerganNational Institutes of HealthBiovision Foundation for Ecological DevelopmentGobierno de AragónEuropean Regional Development FundMinistério da SaúdeFundação de Amparo à Pesquisa do Estado de Minas GeraisEuropean CommissionGojo IndustriesWILEYAgency for Science, Technology and ResearchDepartment of Health, Social Services and Public Safety, UK GovernmentWellcome TrustBoston Scientific CorporationMedical Research CouncilTeva Pharmaceutical IndustriesCarolina Population Center, University of North Carolina at Chapel HillPfizerBiogenBiomedical Research CouncilNorges IdrettshøgskoleAmerican Academy of NeurologyMigraine Research FoundationMerckJapan Society for the Promotion of ScienceGlaxoSmithKlineEli Lilly and CompanyEunice Kennedy Shriver National Institute of Child Health and Human DevelopmenteNeura TherapeuticsMinisterio de Economía y CompetitividadU.S. Department of Health and Human ServicesNational Headache FoundationInstituto de Salud Carlos IIIAmgenAmerican Headache SocietyAlzheimer's AssociationUniversity of New South Wales
KeywordsDemographyHazard ratioEthnic groupApolipoprotein EGerontologyProportional hazards modelMedicinePsychologyDementiaCohortLongitudinal studyInternal medicineConfidence interval

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.306
Teacher spread0.282 · 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
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

Citations16
Published2020
Admission routes1
Has abstractno

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Same venueArchives of Gerontology and GeriatricsSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207