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Record W2761730641 · doi:10.1016/j.schres.2017.09.024

The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project

2017· article· en· W2761730641 on OpenAlexaff
Gabriëlla A.M. Blokland, Elisabetta C. del Re, Raquelle I. Mesholam‐Gately, Jorge Jovicich, Joey W. Trampush, Matcheri S. Keshavan, Lynn E. DeLisi, James Walters, Jessica A. Turner, Anil K. Malhotra, Todd Lencz, Martha E. Shenton, Aristotle N. Voineskos, Dan Rujescu, Ina Giegling, René S. Kahn, Joshua L. Roffman, Daphne J. Holt, Stefan Ehrlich, Zora Kikinis, Paola Dazzan, Robin Murray, Marta Di Forti, Jimmy Lee, Kang Sim, Max Lam, Rick P.F. Wolthusen, Sonja M. C. de Zwarte, Esther Walton, Donna Cosgrove, Sinéad Kelly, Nasim Maleki, Lisa Osiecki, Marco Picchioni, Elvira Bramon, Manuela Russo, Anthony S. David, Valeria Mondelli, Antje A. T. S. Reinders, M. Aurora Falcone, Annette M. Hartmann, Bettina Konte, Derek W. Morris, Michael Gill, Aiden Corvin, Wiepke Cahn, New Fei Ho, Jianjun Liu, Richard S.E. Keefe, Randy L. Gollub, Dara S. Manoach, Vince D. Calhoun, S. Charles Schulz, Scott R. Sponheim, Donald Goff, Stephen L. Buka, Sara Cherkerzian, Heidi W. Thermenos, Marek Kubicki, Paul G. Nestor, Erin W. Dickie, Evangelos Vassos, Simone Ciufolini, Tiago Reis Marques, Nicolás Crossley, Shaun Purcell, Jordan W. Smoller, Neeltje E.M. van Haren, Timothea Toulopoulou, Gary Donohoe, Jill M. Goldstein, Larry J. Seidman, Robert W. McCarley, Tracey L. Petryshen

Bibliographic record

VenueSchizophrenia Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCilagNational Center for Research ResourcesNational Institute of Biomedical Imaging and BioengineeringNational Institute of Mental HealthNational Institutes of HealthH. Lundbeck A/SNational Center for Advancing Translational SciencesMedical Research CouncilHersenstichtingTop Institute PharmaNational Healthcare GroupBundesministerium für Bildung und ForschungTrinity College DublinAcademy of Medical SciencesAmerican Psychiatric Institute for Research and EducationSouth London and Maudsley NHS Foundation TrustNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchUniversity of GalwayBroad InstituteUniversity of PittsburghNational Research Foundation SingaporeMassachusetts General HospitalHarvard CatalystUniversity of MinnesotaNederlandse Organisatie voor Wetenschappelijk OnderzoekHarvard UniversityStanley Medical Research InstituteNational Institute for Health and Care ResearchGlaxoSmithKlinePfizerNational Medical Research CouncilSidney R. Baer, Jr. FoundationNational Alliance for Research on Schizophrenia and DepressionAstraZenecaEli Lilly and CompanyBristol-Myers SquibbU.S. Department of Veterans AffairsHoward Hughes Medical Institute
KeywordsEndophenotypeSchizophrenia (object-oriented programming)NeuroimagingPsychologyNeuropsychologyClinical psychologyImaging geneticsPsychiatryCognition

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)0.007
metaresearch head score (Gemma)0.016
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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.063
GPT teacher head0.376
Teacher spread0.313 · 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

Citations22
Published2017
Admission routes1
Has abstractno

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